I’m kind of new to the world of machine/deep learning so cut me some slack here, but I was wondering the best ways to train models (in my case a transformer) without a GPU. I personally don’t even have a PC, I’ve been using a 2017 MacBook Air. I know deep learning models are quite computationally expensive and since I don’t have access to a GPU, how do I train models? I’ve read about cloud computing services like AWS, Google Colab, etc. but I was wondering what the best method was. Ideally free or as cheap as possible. submitted by /u/Present_Network1959 [link] [comments]  ( 9 min )

We are currently conducting a beta test for our Chrome Extension and we value external input. Our platform allows you to write and receive your gmail emails within the browser. You can also use AI to generate emails, without ever touching gmail or chatgpt. If you're interested in participating, please feel free to message or comment! submitted by /u/Live-Orange-8414 [link] [comments]  ( 9 min )

I have a regression model based on CNN, works reasonably well with less than 1M parameters. I am trying to check how Visual Transformer (ViT) will perform on this task, but due to lack of pooling in ViT, model size is considerably large (~10M parameters). Do ViT have anything equivalent to pooling to reduce number of parameters? If not then that reduces applicability of ViT to large models on large dataset dataset only. For smaller tasks with small dataset, CNN or Resnet are way more computation efficient. Or am I missing something? submitted by /u/Apprehensive-War8915 [link] [comments]  ( 9 min )

For our R&D team, we spent over a month trying to setup our pipeline. After that, we spend at least 5 days after R&D for to put a model into production without the required data pipelines that communicate with our model and the service. For training a model, the infrastructure maintain and manage it also needs to be built for around 2 weeks. Currently, our best solution is to offload the training process by purchasing a GPU and keeping it in the office. submitted by /u/potanees [link] [comments]  ( 9 min )

​ https://preview.redd.it/4t4jbi15rygb1.png?width=2291&format=png&auto=webp&s=f4eedf0d24dee2cbd040b3a19ab9610119b4001e Hi everyone! I want to share some interesting observations that indicate a very simple periodical weights resetting procedure could serve as an implicit regularization strategy for training DL models. This technique also shows potential connection with the Double Descent phenomenon. Here's the link to github etc: https://github.com/amcircle/weights-reset. As a co-author of this study, I must apologize in advance for its brevity. However, I sincerely hope it may prove useful to some. I would gladly respond to your queries and receive your criticism. Your personal experiences related to something similar would also be highly appreciated. submitted by /u/gregorivy [link] [comments]  ( 9 min )

Hi, I am going through research papers and noticed that most of the papers talk about the text conditioned image generation process (reverse diffusion process). The text and time encodings are added as additional channels to the UNet block. However, I am curious to know if any text encodings are used during the training process as well. Is there any preview of the training datasets that is available which is used in the training process ? or a code snippet that points out to the forward part of the training loop Thanks submitted by /u/kaskoraja [link] [comments]  ( 9 min )

Hi everyone, There has been a lot of work on benchmarking autoregressive LLMs, such as HF LLM Leaderboard, but I have not seen much work specifically on the relevancy of such LLMs for retrieval. There is a lot of talk about chat based on knowledge with solutions like llama_index, where LLMs both provide embeddings and answer based on most similar content, but embedding and answer generation need not be the same LLM. I saw the Massive Text Embedding Benchmark (MTEB) but it does not seem to contain a lot of information about the recent autoregressive LLMs. Are the recent autoregressive LLMs, e.g. Llama 2, actually performing better than Bidirectional LLMs such as BERT? Because if so, all the recent fancy chat with your documents projects could use much smaller models to do embedding extraction for retrieval and just call a fancy autoregressive LLM such as GPT4 for answer synthesis. submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

Hi I'm looking for an open source model that can take an image of an info graphic such as a pie chart, graph, etc, and provide a description of the information in that chart. For example the values of the x,y axis and their labels, weather the chart is increasing or decreasing. I've worked with image captioning models such as BLIP before as I have used them in projects involving stable diffusion, but this model doesn't give specifics about the information in the graph, just a brief overview. I know researches have worked on this problem in the past using the vistext dataset: https://news.mit.edu/2023/researchers-chart-captions-ai-vistext-0630 So far I'm thinking that it may come to me finetuning BLIP or equivalent to specialize on infographics instead. Thoughts? submitted by /u/UncleSammmm [link] [comments]  ( 9 min )

I have a significant amount of files that I would like to label for future reference. I've looked at software such as Photoprism or Librephoto which have object classification but they are based on a static model. I'd like something where I could label a few photos then generate similar matches where I can approve the good matches for reinforcement learning. I'm pretty sure I saw a demo like this at a code conference using Azure but I'm hoping for something self-hosted to avoid API fees. I was exploring coding something to do this for me but I don't want to put in the work if something with a UI exists already. This seemed like the best place to ask. submitted by /u/MZZXX [link] [comments]  ( 9 min )

Hi - I have trained a pytorch model that does some fairly simple object classification - The goal is distribute it as part of an app, that will pull information from a user's video. The videos are typically ~25-30 minutes and about 1GB in size, only a 600x600px square on the bottom right of the video is needed for the classification (it's a minimap in a video game) The app is electron based Ideally I want to input a video, and extract the labels from the cropped section once per second. ​ My current attempt involves converting the model to a tensorflowjs model, and rendering the video on a element, stretching it so only the minimap is visible on the canvas, running the model, saving the labels, and increasing the current time of the video by 1 second, and repeating until the video is done. ​ This seems like a terrible plan, but it's much better than the couple of other ideas I've tried (using ffmpeg to extract a frame every second for example) ​ Any advice appreciated! ​ Edit: just to clarify this will only ever be ran on Windows submitted by /u/FreddoRS [link] [comments]  ( 9 min )

Hello, I'm looking for a model (probably two models) that would: Summarize reviews (e.g. website review) to two/three words. Reuse these words or "review tokens" to tag reviews with similar content. Then if a review's content differs (e.g. cosine sim. of 0.2), another tag will be generated from the review that diverges. Is there anything like this on the "market"? submitted by /u/BartPetersyn [link] [comments]  ( 9 min )

Hello to everyone reading this. I am just about to finish Andrew NG's course 3 courses on ML specialization and I have had 2 courses on ML as well in my Business Intelligence Analytics studies at uni. Now I am extremely interested in ML but I see there are wide diaspora of different subfields you can focus on. I need to get into the job market as fast as possible. So can anyone guide me which aspect of ML should I give most of my time to practice and build portfolio that would translate well to interviews and hiring? Thank you submitted by /u/JaguarMoosa [link] [comments]  ( 9 min )

How would you proceed on a project in assessing the difficulty level of a question? I tried using lexicographic metrics like flesh-kincaid score, etc., but those did not yield proper results. Is there a good method I could use? Also, how could I assess the "readability" of a question, or in other words, how easy it is to understand what the question is asking. submitted by /u/uglyboi34 [link] [comments]  ( 9 min )

Hey y'all, this is a model I have been independently building for some time. It uses parts of OpenAI's Jukebox and HarmonAI's Dance Diffusion model. Overall it is a hierarchical latent diffusion modeland generates complete linked musical phrases at good quality. More information as well as examples can be found here: https://medium.com/@jeffsontagmusic/jukebox-diffusion-cbe22ff3cd47 Thanks! submitted by /u/jmoso13 [link] [comments]  ( 9 min )

A lot of my PhD work will be in using different types of ML/NN approaches to characterizing problems in my field. It's kind of weird, since for my undergrad I came from a more traditional science background where we research off papers that were written like 2-20 years ago. Since a lot of these architectures and whatever are updating so fast, I wanted to see if there's a good way to keep up with the latest information so my work wouldn't be outdated by the time I publish. Is there a general workflow that those of you in the field follow in regards to this? submitted by /u/This-Is-My-20th-Acc [link] [comments]  ( 9 min )

Hey everyone, I wrote a blog on LoRA and QLoRA. Hope it helps you in understanding the theory behind them 🤗 https://medium.com/@gitlostmurali/understanding-lora-and-qlora-the-powerhouses-of-efficient-finetuning-in-large-language-models-7ac1adf6c0cf If the above one is behind paywall, you can visit the blog here (https://gitlostmurali.com/machine-learning/data-science/lora-qlora) submitted by /u/Outlandish_MurMan [link] [comments]  ( 8 min )

Hey all, like the title says, I’m losing my voice due to an illness (Parkinson’s disease), and I would like to create an AI voice using recordings from 10 years ago. I used to be a prolific podcaster, and I have about 50 episodes of podcasts that I can use as input. Is this possible? What service or software can I use? My voice is beyond repair since Parkinson’s is a progressive disease. An AI voice would allow me to work and would open up new doors for me. Thank you! submitted by /u/NWMoney101 [link] [comments]  ( 9 min )

Candle is a minimalist ML framework for Rust Some of its features Examples of popular models: Whisper, Llama 2, Falcon, Bert, Starcoder WASM support, so you can run the models directly in the browser User-defined kernels, so you can use Flash Attention Similar syntax to PyTorch Data loaders Transformer utilities submitted by /u/hackerllama [link] [comments]  ( 9 min )

Hey all, I have a technical background, having studied engineering and ML at one of the world's leading universities. I really enjoyed it and did well, but long story short, since graduating (coming to 2 years) I have been working in a Family Office, doing things I don't feel are very related. I wanted to know what kind of things I can do to keep myself in the loop and continue developing my ML/DS skills in my spare time. Alternatively, ideas of projects I could have just to make sure I have a portfolio? submitted by /u/thegreatudini [link] [comments]  ( 9 min )

Typically, research papers and leaderboards only report the overall score on Measuring Massive Multitask Language Understanding (MMLU) and not per task performance. Hugging Face recently released detailed evaluation data that includes per task performance. I made a sortable leaderboard here https://huggingface.co/spaces/CoreyMorris/MMLU-by-task-Leaderboard . You can also make custom scatter plots on the site so you can explore the relationship between parameter count and performance. submitted by /u/corey1505 [link] [comments]  ( 9 min )

I've realized my technical understanding of explainability is a few years behind, having last focused on it with LIME and Shap. Does anyone have a survey reference they like for recent trends and updates in ML explainability? submitted by /u/balcell [link] [comments]  ( 8 min )

At first, with InstructGPT and ChatGPT, it looked like RLHF was the holy grail to successfully finetune LLMs on human preferences. Then, from May 2023 onwards, a trend of doing just SFT with high-quality data showed up (e.g. "LIMA: Less Is More for Alignment" https://arxiv.org/abs/2305.11206) as an alternative to doing RLHF. What's your opinion on these two narratives? Is RLHF likely to still be relevant even in the presence of SFT with high-quality data? submitted by /u/bornot2b [link] [comments]  ( 9 min )

Hi all, I am currently already working in the field of ML research at a big name medical research center. Our main focus is in application of ML methods with the focus on stroke diagnostics and treatment. Now, I am quite happy working here but my background is somewhat interdisciplinary. I have a bachelor's in Life science and a Master in bioinformatics. Because of this I always feel like I have to catch up to my colleagues when it comes to ML and in parts also computer science knowledge. It feels like there are a million things to learn and many small details to know that I am not even sure how to look up. I am curious what your experience has been if you were/are in a similar situation? How did you manage to catch up? submitted by /u/JuicyLambda [link] [comments]  ( 9 min )

Either I'm too new to the space, or I'm stating the obvious, but it seems that object detection performance is really low. The SOTA currently is 66% on COCO test-dev, which doesn't match how well it seems like AI is currently performing with self-driving cars, surveillance tech, and others. Am I missing something? submitted by /u/philipkd [link] [comments]  ( 9 min )

I. Introduction This paper aims to provide an in-depth explanation of representing and propagating wavefunctions in a hierarchical manner using Gaussian basis functions. Wavefunctions are mathematical descriptions of the quantum states of physical systems and are fundamental to quantum mechanics. However, representing complex wavefunctions for real-world quantum systems remains a key challenge. This paper proposes using multiple layers of Gaussian basis functions, with trainable amplitudes, to represent wavefunctions in a hierarchical fashion and enable wavefunction propagation between layers. Understanding wavefunction representation and propagation has significant implications in diverse fields like quantum computing, quantum chemistry, and materials science. Efficient wavefunction man…  ( 12 min )

I’ve been researching the Evol-Instruct datasets now for a few days and have decided I want to build my own out for a specific use case. I’ve read literally everything possible, admittedly not much outside of WizardLM and GeorgiaTech, but I’ve read it. I was hoping to discuss it here with smarter people. I’m seeing this as a way to use LLMs to generate great datasets. However, my use case doesn’t really exist in any models yet. Not thoroughly enough to produce a good Evol-Instruct set. So, I’m going to do that tomorrow. I’m going to use The Blokes WizardCoder-Guanaco 15b GPTQ version to train on my specific dataset - about 10GB of clean, really strong data I’ve spent 3-4 weeks putting together. In theory, I’ll use the Evol-Instruct script from WizardLM to generate the new dataset, and then I’ll apply that to whatever model I decide to use. There is a good chance I train my own on general Evol-Instruct datasets available now, and likely quite a large one. I’m looking for any tips, discussion, ideas, thoughts from the community. Cheers! submitted by /u/LoadingALIAS [link] [comments]  ( 9 min )

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All services I found made the blurry text even worse. Is there any which has good results for documents? submitted by /u/_SarahB_ [link] [comments]  ( 8 min )

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Hi there 👋, I'm excited to share a project I've been working on over the past few months! My primary goal is to create a service that will be beneficial for people. Please share your thoughts on this idea, and suggest any new features you think I should implement! Exciting Features: • Speak to Write: with this feature, you can speak your thoughts or information and the tool will transcribe it into text. The best part? You can then forward the transcribed text to any application with just one click. • Audio to Action Plan: the service can transform a received audio message into a structured list of elements or bullet points. This feature is especially useful for outlining an action plan or item list. • Speak in and Language: you can dictate an audio message in your native language, and the service will translate it into any other language, maintaining high translation quality—significantly better than Google Translate. • Meeting Transcripts & Summaries: the service is perfect for converting recorded audio from meetings into text and generating concise summaries. It supports the upload of users' files. Thank you for taking the time to check it out. I look forward to hearing your feedback. You can access the service by visiting this link: https://audionotes.ai submitted by /u/OneMoreSuperUser [link] [comments]  ( 9 min )

I have a killer project idea but it requires fully custom image generation. Character portraits. Any API like that out there? submitted by /u/thedarklord176 [link] [comments]  ( 8 min )

I just want a script to perform the task not AI itself so that I have something reliable. It always puzzles me why these things don't instantly pop up as services where I don't have to worry about even deploying the script (but that's another issue). submitted by /u/VLADIMIROVIC_L [link] [comments]  ( 8 min )

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Tutorial about creating images using ChatGPT. submitted by /u/SplitYOLO [link] [comments]  ( 8 min )

Hello, I'm currently editing videos using capcut, which is not ideal. I'm looking for an ai, that ideally : Finds me B-roll according to what I speak. Cuts "bad takes" out Good captions "TikTok style" Audio enhance. Do you guys know anything like this? Thank you! submitted by /u/Orlandostyler [link] [comments]  ( 8 min )

I've been using this today whilst I've been working and I found it pretty comical at first with the voice that talks to you, but now I'm starting to love it! I want it to talk more when it does talk. It feels like a nice break in the music to have the AI talk like a radio host. I'm sure some people would rather that not being a feature (if they use it at all), but I'd love for it to have some more comedic one-liners, possible news updates, and potentially traffic updates based on location and if it knows you're driving. Would be awesome! It's also a really good tool for if you want to listen to music you've not heard before. Whether it's part of your usual genre or not. Looking forward to seeing how this progresses! submitted by /u/Columbian_Toad [link] [comments]  ( 9 min )

Hi everyone. I am an artist. And programmer, and kind of a bit of everything. But what is important, is that I was an artist before the current "generative AI" was a thing, and I have been drawing, digitally and traditionally alike for like... a decade? Art, to me, is getting what is inside your head, and presenting it to others outside of your consciousness and thoughts. It's showing the world a piece of your interpretation, your experience, your impressions of the world you inhabit. It's about communicating to others your emotions, your ideas, your thoughts and feelings. Not everyone can draw, or paint, or sculpt. I could say "learn it, it's easy", but that would be a lie. It isn't easy. It is years upon years of constant, hard work, requiring focus and dedication, and a passion for l…  ( 11 min )

submitted by /u/DarronFeldstein [link] [comments]  ( 8 min )

How do I make AI-generated videos with prompts for free? submitted by /u/DankDude6T9 [link] [comments]  ( 8 min )

Hello AI enthusiasts! I'm a software engineer passionate about AI, and recently I've been experimenting with making my first AI game. In the game, you try to negotiate a price down on a watch with an AI-driven salesman, rewarding -or roasting lol- you depending on your bargaining skills. I’d be more than happy to get your thoughts and feedback on this idea, it's the first application I've built using AI so any tips would be much appreciated! Thanks! submitted by /u/gavo_gavo [link] [comments]  ( 9 min )

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I want to start making a YouTube channel, because I've got a passion project I want to work on with a Minecraft modpack. Obviously, Minecraft is a HUGE game and has thousands of videos posted every day... This is why I want to know if there is an AI that can rate videos based on editing, audience engagement, sound, etc... Also giving areas of improvement and the strengths of the video. Probably a big ask and SO far fetched, but there's always a chance of something being out there. submitted by /u/Columbian_Toad [link] [comments]  ( 9 min )

Can someone recommend a great AI photo editor that can take 100 profile photos and standardise them, IE crop so head is same size across all photos, background removed and placed on standard back ground. submitted by /u/Woodger [link] [comments]  ( 8 min )

​ https://preview.redd.it/9epla7xjdugb1.png?width=960&format=png&auto=webp&s=92190970027b08476ac9899a42d7099fe67cf5aa submitted by /u/Maxie445 [link] [comments]  ( 8 min )

Data analytics company Qureight has entered into a multi-year strategic research collaboration with AstraZeneca that will use AI models to accelerate research into lung diseases.[1] Zoom’s terms of service update establishes the video platform’s right to use some customer data for training its AI models.[2] Cigna, one of the country’s largest health insurance companies, faces a class action lawsuit over charges that it illegally used an AI algorithm to deny hundreds of thousands of claims without a physician’s review.[3] Japan plans guidelines for AI-savvy human resources.[4] Sources: [1] https://www.digitalhealth.net/2023/08/qureight-collaborates-with-astrazeneca-for-ai-lung-disease-research/ [2] https://www.cnbc.com/2023/08/07/zoom-ai-tools-trained-using-some-customer-data.html [3] https://www.medicaleconomics.com/view/cigna-using-ai-to-reject-claims-lawsuit-charges [4] https://asianews.network/japan-plans-guidelines-for-ai-savvy-human-resources/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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Posted by Fuzhao Xue, Research Intern, and Mostafa Dehghani, Research Scientist, Google Adaptive computation refers to the ability of a machine learning system to adjust its behavior in response to changes in the environment. While conventional neural networks have a fixed function and computation capacity, i.e., they spend the same number of FLOPs for processing different inputs, a model with adaptive and dynamic computation modulates the computational budget it dedicates to processing each input, depending on the complexity of the input. Adaptive computation in neural networks is appealing for two key reasons. First, the mechanism that introduces adaptivity provides an inductive bias that can play a key role in solving some challenging tasks. For instance, enabling different num…  ( 93 min )

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​ https://preview.redd.it/xdp3bkwwpvgb1.jpg?width=2800&format=pjpg&auto=webp&s=513a63ed81eec85e6bc254f84e4208094afc7d4a Very interesting blog post from OpenCV.ai team about how can explore ChatGPT to serve for code development debugging. Introduction from the article: As programmers, we often work with familiar development environments, but occasionally we encounter new tools that can be time-consuming and challenging to learn. In such situations, having virtual assistance can be extremely beneficial. In this article, I will share my experience of contributing to OpenCV, a renowned open-source library, despite having limited knowledge of C++ and understanding its architecture. I achieved this with the assistance of ChatGPT, a Large Language Model (LLM). I hope you can find it interesting. More details are here. submitted by /u/No-Independence5880 [link] [comments]  ( 9 min )

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I want to study Reinforcement Learning, but the concepts are really hard and mathematical. Whenever I think I grasp something I forget it the next day completly. The Basic Concepts of MDP is the only thing which I think I understood. But I cant understand the Training algorithms like Sarsa or Q-Learning and DQN and their implementations. I am really frustrated and overwhelmed. Does anyone know some good resources to understand the concepts and implementations of RL? submitted by /u/Menium [link] [comments]  ( 9 min )

In Q-learning (image), episodes are run, in the sense that, the states are visited in the order they appear as part of one sequence in an episode. In Dyna-Q (image) (which is btw described to be the same as Q-learning when the planning portion is deleted), there doesn't seem to be any iteration over the states of an episode. It just picks a state, applies the e-greedy policy on it to choose the action, learns, updates the model, then plans. Would Q-learning also work fine if we got rid of the "episodes" and just picked isolated state-action pairs? Thank you submitted by /u/AstronautVarious3791 [link] [comments]  ( 9 min )

I know for image recognition there is a rough intuition that neural network lower layers learn low level features like edges, and the higher layers learn more complex compositions of the lower layer features. Is there a similar intuition about what a value network or policy network learns in deep RL? If there are any papers that investigate this that would be helpful submitted by /u/Turkeydunk [link] [comments]  ( 9 min )

This post will discuss two scripting languages, but that’s not what the post is really about. It’s really about expressiveness and (or versus) productivity. *** I was excited to discover the awk programming language sometime in college because I had not used a scripting language before. Compared to C, awk was high-level luxury. Then a […] Productive constraints first appeared on John D. Cook.  ( 6 min )

A Möbius transformation is a function of the form where ad – bc = 1. We usually think of z as a complex number, but it doesn’t have to be. We could define Möbius transformations in any context where we can multiply, add, and divide, i.e. over any field. In particular, we could work over […] Möbius transformations over a finite field first appeared on John D. Cook.  ( 6 min )

Announcements Top Stories In-Depth The post DSC Weekly 8 August 2023 appeared first on Data Science Central.  ( 20 min )

Prompt engineers are emerging as key players in the development and optimization of AI models as artificial intelligence (AI) continues its evolution and becomes an integral part of various industries. As experts at crafting effective prompts, they have been instrumental in shaping the future of artificial intelligence through their ability to enable models to deliver… Read More »The emergence of prompt engineers: The next in-demand role in AI The post The emergence of prompt engineers: The next in-demand role in AI appeared first on Data Science Central.  ( 22 min )

As generative AI continues to sweep an increasingly digital, hyperconnected world, NVIDIA founder and CEO Jensen Huang made a thunderous return to SIGGRAPH, the world’s premier computer graphics conference. “The generative AI era is upon us, the iPhone moment if you will,” Huang told an audience of thousands Tuesday during an in-person special address in Read article >  ( 9 min )

Machine learning helped Waseem Alshikh plow through textbooks in college. Now he’s putting generative AI to work, creating content for hundreds of companies. Born and raised in Syria, Alshikh spoke no English, but he was fluent in software, a talent that served him well when he arrived at college in Lebanon. “The first day they Read article >  ( 6 min )

Organizations across industries are using extended reality (XR) to redesign workflows and boost productivity, whether for immersive training or collaborative design reviews. With the growing use of all-in-one (AIO) headsets, more teams have adopted and integrated XR. While easing XR use, AIO headsets have modest compute and rendering power that can limit the graphics quality Read article >  ( 6 min )

Professionals, teams, creators and others can tap into the power of AI to create high-quality audio and video effects — even using standard microphones and webcams — with the help of NVIDIA Maxine. The suite of GPU-accelerated software development kits and cloud-native microservices lets users deploy AI features that enhance audio, video and augmented-reality effects Read article >  ( 8 min )

AI and accelerated computing were in the spotlight at SIGGRAPH — the world’s largest gathering of computer graphics experts — as NVIDIA founder and CEO Jensen Huang announced during his keynote address updates to NVIDIA Omniverse, a platform for building and connecting 3D tools and applications, as well as acceleration for Universal Scene Description (known as OpenUSD), the open and extensible ecosystem for 3D worlds.  ( 10 min )

Picture this: Creators can quickly create and customize 3D scene backgrounds with the help of generative AI, thanks to cutting-edge tools from Shutterstock. The visual-content provider is building services using NVIDIA Picasso — a cloud-based foundry for developing generative AI models for visual design. The work incorporates Picasso’s latest feature — announced today during NVIDIA Read article >  ( 6 min )

NVIDIA researchers are taking the stage at SIGGRAPH, the world’s largest computer graphics conference, to demonstrate a generative AI workflow that helps artists rapidly create and iterate on materials for 3D scenes. The research demo, which will be presented today at the show’s Real-Time Live event, showcases how artists can use text or image prompts Read article >  ( 6 min )

DENZA, the luxury EV brand joint venture between BYD and Mercedes-Benz, has collaborated with marketing and communications giant WPP and NVIDIA Omniverse Cloud to build and deploy its next generation of car configurators, NVIDIA founder and CEO Jensen Huang announced at SIGGRAPH. WPP is using Omniverse Cloud — a platform for developing, deploying and managing Read article >  ( 5 min )

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you […]  ( 7 min )

Artificial intelligence (AI) adoption is accelerating across industries and use cases. Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative AI is allowing customers to use advanced state-of-the-art solutions with almost human-like performance. These complex models often require hardware acceleration because it enables not only faster training but also faster inference […]  ( 13 min )

Here's the pitch: We made an editor called Gamut that lets you enter your ideas in any form you want. Bullets, carefully constructed paragraphs, it doesn’t matter. Then, our patent-pending technology lets you convert to prose and adjust, shaping the text like a graphic designer shapes an image. We want r/MachineLearning's advice and field experience, because tbh we're just a bunch of teenagers who haven't even gone to college yet. Check it out: gamut.ink submitted by /u/gamut_ink [link] [comments]  ( 9 min )

I was just watching the documentary about the 'greatest movie never made', director Alejandro Jodorowsky's vision of Frank Herbert's Dune. There is a huge book that contains a storyboard version of the movie with lots of production art by artists Moebius, Chris Foss and HR Giger. The movie was to star Jodorowsky's son as Paul Atriedes, Salvadore Dali as the Emperor, Orson Wells as Baron Harkonnen and Mick Jagger as Feyd. Could one of today's AIs be 'fed' Jodorowsky's book and create a movie of his vision? Curious to know what your opinions are on this. Thanks. submitted by /u/shopdog [link] [comments]  ( 9 min )

Hi, all, I would like to use batch reaction trend data to build a regression model. I'm wondering what is the best way to approach this. Here's some background: Reaction Data: Time (min) Pressure (bar) Temperature (°C) Flow (kg/h) Gas Total (kg) 1 10 70 502 8 2 10.1 71 498 16 ... ... ... ... ... 102 10.3 76 475 850 Output: Polymer property X The reaction continues until a gas total is met and the time this takes depends on the other variables. I have ~700 batches of data in a format similar to the above and would like to predict polymer property X. As the variables can change minute to minute I was thinking of binning the variables into 5 minute bins using the mean and using these as variables for linear regression or similar. Is this a valid approach or is there another way I can approach the problem? Thanks! submitted by /u/Nefarious_P_I_G [link] [comments]  ( 9 min )

Hi everyone, I have put together a repo that provides comprehensive resources for Out-of-distribution Detection, Robustness, and Generalization. The repo contains articles, talks, libraries, papers, etc. Check it out. https://github.com/continuousml/Awesome-Out-Of-Distribution-Detection submitted by /u/Ok-Kaleidoscope-505 [link] [comments]  ( 8 min )

Alexander Amini, a Postdoctoral Associate at MIT, well known for the MIT's Introduction to Deep Learning Course, published a git repo called CAPSA for uncertainty prediction. This was introduced during the online course. The code was released under Thermis AI, Inc, a private company. He is the co-founder and CSO of the company. You can check how well the code was documented in the wayback machine. Recently, they removed the code base from the github and launched a pro version with selected companies as beta. The original repo (now called capsa-lite) was a great learning tool that I wanted to use. This was a quick way to try out different methods of uncertainty prediction using minimal code. Unfortunately, they have pulled all previous version of the code from the github repo. I was wondering if anyone knows a similar python package or has the old repo - would be really helpful! submitted by /u/shikamaru_77 [link] [comments]  ( 9 min )

I'm putting together an ML workstation primarily focused at handling CNN and Transformer workloads. Component selection so far: https://de.pcpartpicker.com/list/zVPtt7 I've got a couple of questions specifically regarding the motherboard. One concern I have is whether the space between the two GPUs is sufficient, as I'm planning to set them up using NVLink. Additionally, I'm curious about the compatibility of the case and motherboard for effective air cooling ( not considering water cooling at the moment). Anyone else with dual 3090s who can give some insights on how they've managed temperatures and potential overheating issues? Lastly, would upgrading to a Ryzen 9 5900X prevent me from bottlenecking the GPU's? Would love to hear your feedback and suggestions! submitted by /u/Hugejiji [link] [comments]  ( 9 min )

i want to fine tune any open source llm for code generation purpose with some of my code. any idea what model would be suitable? and any example of implementation? submitted by /u/learner_beginner [link] [comments]  ( 8 min )

Anyone here know what the trends are towards hiring for an AI/ML position without a PhD? Is it advisable to get a PhD if you want to be in the field and keep rising within it? submitted by /u/CleanGarden7051 [link] [comments]  ( 8 min )

Hi, I'm about to graduate in Physics (PhD). I am an experimentalist with a background in electromagnetic. I am trying to apply for jobs, but there are some few options for physicists (based on my geography). So, I am trying to learn some new skill for my future job. One option would be Machine Learning, which is on-demand and the field is growing. The other option is Quantum Computing. I can start a postdoc in quantum information theory as well. Each path, has its pros and cons, and the final decision is based on many factors. I just don't have enough data and information to say which one is more secure in the future? Which one has less compete? And also, is it possible to get hired without any serious project in ML, and just self-taught? If you were me, which one would you pick? Thanks submitted by /u/Jaded-Membership-602 [link] [comments]  ( 9 min )

What is a typical non-academic ML salary with a PhD... ... immediately after completing the PhD? (Assuming no academic positions ever post PhD.) ... after 10 years of experience? ... in biotech specifically? (More, less, or the same as average?) submitted by /u/Practical_Tea_3779 [link] [comments]  ( 8 min )

Hi all, I'm looking apply to some top masters for machine learning in the UK, so I'm guessing you know which one I'm referring to. I got some guidance from the application advisor, which state they like to look at the transcript the most to have an idea of my linear algebra, calculus and statistics ability. I got 70% in "Maths for Computer Science" and some other modules I strong first and 2:1 in some others, but in general my course wasn't too mathematically intensive. I did BSc Computer Science. I have been working as SWE the past 3 years. I have completed the following specialisation "Mathematics for Machine Learning and Data Science Specialization" and read "Mathematics for Machine Learning", as learning about mathematics actually got me into ML. I have also covered the videos on 3Blue1Brown etc. The application advisor said that certs don't really mean too much which is understandable. I can't change the past in terms of BSc transcript, therefore I was thinking a project may be a good way to showcase this. Any tips on how to best showcase this or get across my ability would be extremely helpful? submitted by /u/DNOFHF [link] [comments]  ( 9 min )

Hey, I've been trying to build an ML workstation and was considering the idea of using two RTX 3090's to get the extra VRAM instead of a single 4090. However, I've come across some confusion regarding whether they can share their VRAM or not. Do I need to run them via NVLink to achieve this? I believe PyTorch's data parallelism splits the batches across both GPUs, but that wouldn't effectively combine their VRAM right? Any advice or insights you can share on the topic would be highly appreciated! submitted by /u/Hugejiji [link] [comments]  ( 9 min )

I'm considering getting a graduate degree in ML. However, I am not very interested in NLP or academic research. I would like to learn things that are relevant to the intersection of ML and genomics or medicine. Are there any graduate programs/degrees to this effect? If so, which ones? submitted by /u/Practical_Tea_3779 [link] [comments]  ( 8 min )

Hi folks. I am sure you know the running gags around “thin OpenAI wrapper” products. Instead of more toy products, I am doing an experiment with some “AI engineering” to come up with a solution that’s closer to being usable in actual production cases. My background is in project management and data engineering, and I’ve built large systems for big companies and worked as a consultant in the space. I’ve seen enough crappy data pipelines for a lifetime. Hence. I want to do something different: A thin AI wrapper is not sufficient for having reliable data pipelines that use OpenAI for schema management and inference So this leaves me with the following doubts: ​ How to scale code horizontally and vertically? Using third-party solutions? SNS/SQS/Kafka? How to log and trace? Langsmith? Custom solutions? How to extend reliably with my own data, and make it stateful? Looking for your perspective ​ What do you think about the state of data engineering, MLOps, and infrastructure in AI companies? What do you think about how to scale properly the systems and prepare them for the future? In this code here, I do process some PDFs as a simple pipeline, what approaches do you think could be better? My current thinking and the state of the project ​ I should create a formal scale of usability. I am looking for your input here. I should improve model consistency, extends the model with custom domain knowledge, and make an early attempt to build simple user agents in the domain What I have is a schema inference, contracting basics, and a way to structure unstructured data I’m about to create a memory component that manages the data stored in vector dbs, as a DWH for AI If I bring this use case that was not something available easily to the public before, how best do it? Links: If you like my project, please give it a star :) my git repo submitted by /u/Snoo-bedooo [link] [comments]  ( 9 min )

Hey there, I got 2x 4090 RTX with 24GB GDDR each.I often ran into the problem of CUDA out of memory. Tried to allocate X MiB (GPU 0; 23.65 GiB total capacity; 22.75 GiB already allocated; 96.81 MiB free; 22.76 GiB reserved in total by PyTorch) I wonder if there is a way to take usage of both GPUs so the model is split onto both GPUs. When training models I use torch.nn.DataParallel to use both GPUs, but it seems like I am not doing it right for load the model. Can anyone help me? Both GPUs are available in the system - this has already been checked. submitted by /u/Sensitive_Limit1620 [link] [comments]  ( 9 min )

Hey folks, We're looking for people to join our research group. We are passionate about fine-tuning LLMs for downstream tasks, specifically LLAMA for imitating chat behaviour (being constraint aware). ​ The end goal is to build an open source app where you can clone and upload your chat history (say from Whatsapp) and it starts to answer like you Do let me know if it sounds interesting and you'd like to join us... https://preview.redd.it/sdo42mx1yogb1.png?width=1280&format=png&auto=webp&s=9de5008ed8ed18cedb25034d68984cb11e2a6a12 submitted by /u/im_datta0 [link] [comments]  ( 9 min )

humanscript is an inferpreter. A script interpreter that infers commands from natural language using AI. There is no predefined syntax, humanscripts just say what they want to happen, and when you execute them, it happens. https://github.com/lukechilds/humanscript This is a humanscript called tidy-screenshots. It takes an unorganised directory of screenshots and organises them into directories based on the month the screenshot was taken.It can be executed like any other script. https://preview.redd.it/2b0oz2kgwogb1.png?width=1576&format=png&auto=webp&s=9285805a1d0668ae5fe300857f9b67161b8ecda4 The LLM inferpreted the humanscript into the following bash script at runtime. ​ https://preview.redd.it/x8hwdrzhwogb1.png?width=2188&format=png&auto=webp&s=5fcba87a9606a446d169e8ae37b5c8c251525e5e The code is streamed out of the LLM during inferpretation and executed line by line so execution is not blocked waiting for inference to finish. The generated code is cached on first run and will be executed instantly on subsequent runs, bypassing the need for reinferpretation. ​ https://i.redd.it/t6b1stbkwogb1.gif The humanscript inferpreter supports a wide range of LLM backends. It can be used with cloud hosted LLMs like OpenAI's GTP-3.5 and GPT-4 or locally running open source LLMs like Llama 2. You can run humanscript in a sandboxed Docker environment with a single command if you want to have a play. https://github.com/lukechilds/humanscript#install-humanscript submitted by /u/dyslexiccoder [link] [comments]  ( 9 min )

lets say i have a set of images which contains sentences of text on it. now i want to generative images using some generative model with valid (meaningful) text in them. what i assume is just using gan or more powerful diffusion to generate images but i don't think the generated images won't contains valid text in them. i want the model to implicitly learn the text in the images without feeding external text or ocr on them. does any one know any paper trying to tackle this problem. any comments on this by anyone. submitted by /u/specializedboy [link] [comments]  ( 9 min )

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Hey folks, For the past 2 years I've been working on a library to automate the most tedious part of my own work - data loading, normalisation, typing, schema creation, retries, schema inference, evolution & ddl generation, self deployment.. Basically, as you build better and better pipelines you will want more and more, and dlt supports those options. The value proposition of this library is to automate the tedious work you do, so you can focus on better things. What's special about dlt? In the easiest form, you shoot response.json() json at a function and it auto manages the typing normalisation and loading, kind of like a pandas df.to_sql() but with auto schema inference, versioning and evolution. It supports loading to files, databases, and soon table formats and vector dbs. In its most complex form, you can do almost anything you can want, from memory management, microbatching, multithreading, extraction DAGs, 1 line Airflow/git actions deployment, dbt runner, streamlit app for data discovery, sql client, atomic state dictionaries, etc. The library is in use with early adopters, and we are now working on expanding our feature set to accommodate the larger community. We are adding Athena + Iceberg and Weaviate vector dbs next. Free forever The library is open source and will forever be open source. We will not gate any features for the sake of monetisation - instead we will take a more kafka/confluent approach where the eventual paid offering would be supportive not competing. Call for Feedback! Feedback is very welcome and so are requests for features or destinations. I would particularly love to hear from you: What destinations are you looking for from such a tool? And what use cases do you usually have? I'm a data engineer so my knowledge is more around loading external sources to a common space. Links Colab demos: Load to duckdb with schema evolution Docs main page Thank you in advance for your feedback! submitted by /u/Thinker_Assignment [link] [comments]  ( 9 min )

https://textadventure.v5games.com/ Hi All, I created these Text Adventure Games with AI, some help from the community which designs prompts+some avatars. The AI Characters can be created with an AI Art Generator. Voices and Illustrations are done using AI https://textadventure.v5games.com/ Let me know what you think! submitted by /u/BoxOrigi [link] [comments]  ( 8 min )

Staying on top of all changes, tools, and best practices with AI is getting increasingly hard. Each week I find just 1 piece of information that is most interesting across research, products, business news, and many more. No fluff guaranteed. Sharing the top research from this week's edition: https://preview.redd.it/fa3b1u39nlgb1.png?width=591&format=png&auto=webp&s=1ccd78136e3578396878fd9641605845f0309865 Summary: Meta released their latest open-source model, Llama 2, in partnership with Microsoft’s Azure platform. But Microsoft also offers OpenAI models and is a major investor in the company (they paid $14B for 49%). So, confused Matt asks, why would Microsoft partner with Meta, when it might undermine their investment in OpenAI? 💡 Answering the question: Spreading the risk: OpenAI may have the first mover advantages, but this does not always last (e.g. Blackberry, Myspace, Yahoo). Microsoft is betting on AI but keeps the chips diversified on multiple players. It’s beside the point: regardless of who Microsoft supports, their game is to attract all AI utilization on Azure. It's not about the tools but about the CPU/GPU cycles they can charge for. smart! The real AI gangsta: Microsoft is sitting on the holy trinity of AI now. Exclusive partnerships with top LLMs (OpenAI, Meta) Priority access to Nvidia GPUs And strategic assets like GitHub and Azure View tweet If you'd like weekly recaps like this sent to your inbox, consider subscribing to the Tomorrow Now newsletter. 😄 submitted by /u/TomorrowNowTech [link] [comments]  ( 9 min )

Microsoft Azure users can now turn to the latest NVIDIA accelerated computing technology to train and deploy their generative AI applications. Available today, the Microsoft Azure ND H100 v5 VMs using NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking — enables scaling generative AI, high performance computing (HPC) and other applications with a Read article >  ( 5 min )

I want an app that can draw a widemouth bass smoking a blunt. All the free ones ive tried give me supid anime girls when all I want is fish submitted by /u/Barefoot_slinger [link] [comments]  ( 8 min )

I’m interested in trying out a subscription-based generative AI service. Candidates include (but are not limited to) CoPilot, ChatGPT pro (or whatever it’s called), and Midjourney. Which generative service do you think is most worth the cost? submitted by /u/galactictock [link] [comments]  ( 8 min )

Is there any Free AI Voice Cloner for free, that allow me simply to install the Exe? And Has option to input my Voice to it that I record? I dont have any coding and command skills. so is there something simple to install? Thanks for Answers submitted by /u/Matejsteinhauser14 [link] [comments]  ( 8 min )

I’ve used several AI programs that work excellent on blurred/pixelated photos of human faces but beyond that, I have not had success finding a program that can render animals in similar way. I’m more looking for something that can make the quality of a pixelated photo of say, a dog, non pixelated. Or at least, much less pixelated. The images I’m trying to use are just absolutely horrible and not fixable, or I am just not using the best programs for my purposes. Or the programs I’m looking for simply do not exist yet. If you have any recommendations (Paid or free programs) please do share! I have a MacBook and an iPhone if that helps. Thank you! 💕 submitted by /u/briannaleidy [link] [comments]  ( 9 min )

submitted by /u/dyslexiccoder [link] [comments]  ( 8 min )

Hello, im in need of an ai that could rewrite for example a pdf document changing the wording but keeping the meaning of the content. Right now im a user of chatgpt plus, and trying to use code interpreter for that, ive managed to get what i want, but it isnt capable of rewriting more than two pages without crashing or simply stoping the process without any warning. I do not know if im using the prompting in a wrong way, any help would be apreciated, also, in case theres an ai out there capable of doing this in a better way id be glad to know about it. Thank you guys. submitted by /u/namelessgang [link] [comments]  ( 9 min )

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"It looks likethisis some sortof programming syntax maybe JavaScript perhaps? Let metell ya though buddy dat aintmuch informatio todo wit. Wouldya care ta tell mesomewhat ye wanna know boot heck, might make things easier ferus botsto give yo useful responses faster innasecondsoffuture interactions brotha man :)" Anyone else used this site? It's through a site called nimblebox.ai, they have different models and allow you to adjust the temperature submitted by /u/jordan_jpg [link] [comments]  ( 8 min )

submitted by /u/wolfdeathkill [link] [comments]  ( 8 min )

Don’t believe everything you hear in the media… I learned this firsthand. This one time I accidentally went on Jessie Waters…for real 😂 https://youtu.be/1X31DHV0gyg?si=fU8p2D4-ShTWUdQs https://open.spotify.com/episode/1M6dbrrP4EoudfTUvD4BqF?si=YMBCFXYfTsmUeOXAe_-lMg submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

I have these chiptune songs I made myself, and I want to know if there is any AI that can remaster them with real instruments, etc., like an old 8-bit video game song that is updated to a modern version in a remake. Is any already AI capable of doing that? submitted by /u/Severo_ [link] [comments]  ( 8 min )

A common idiom in command line processing of text files is ... | sort | uniq | ... Some process produces lines of text. You want to pipe that text through sort to sort the lines in alphabetical order, then pass it to uniq to filter out all but the unique lines. The uniq utility […] Sort and remove duplicates first appeared on John D. Cook.  ( 5 min )

Fair Data Forecast Interview with Gregor Stühler of Scoutbee Scoutbee’s CEO and founder, Gregor Stühler, who has a background in computer science and  electrical engineering, first learned about the challenges of procurement and supply base management as a project engineer for a multinational medical device company. Scoutbee’s focus on solving supply base problems through hybrid… Read More »Scaling Supply Base Data and Reuse with Knowledge Graphs and LLMs The post Scaling Supply Base Data and Reuse with Knowledge Graphs and LLMs appeared first on Data Science Central.  ( 19 min )

The video gaming industry has an estimated user base of over 3 billion worldwide1. It consists of massive amounts of players virtually interacting with each other every single day. Unfortunately, as in the real world, not all players communicate appropriately and respectfully. In an effort to create and maintain a socially responsible gaming environment, AWS […]  ( 13 min )

Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.  ( 9 min )

Hello everyone! I'm a newbie and making my project on machine learning and the aim is create a programme to recognise a spice by feeding some chemical constituents, but I can't find appropriate dataset for it. I have been searching for months, and now I'm a bit desperate, so I'm asking anyone interested for help... I know maybe it was a mistake to choose exactly this topic, but I can't drop the project. submitted by /u/Acceptable-Muscle-98 [link] [comments]  ( 8 min )

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Hello I am attempting to reduce a matrix that is 57 by 256 to 57 to 128. I was attempting to use PCA but it failed as maximum size would be 57 by 57. I was also attempting an autoencoder but the syntax behind this is very confusing so If anyone could give me adivce that would be great. Thank you submitted by /u/amayorgafcw [link] [comments]  ( 8 min )

Hello, I have been working on an OpenAI-compatible API for serving LLAMA-2 models written entirely in Rust. It supports offloading computation to Nvidia GPU and Metal acceleration for GGML models ! Here is the project link: Cria- Local LLAMA2 API You can use it as an OpenAI replacement (check out the included `Langchain` example in the project). This is an ongoing project, I have implemented the `embeddings` and `completions` routes. The `chat-completion` route will be here very soon! Really interested in your feedback and I would welcome any help :) ! ​ ​ submitted by /u/amindiro [link] [comments]  ( 9 min )

submitted by /u/eusben [link] [comments]  ( 8 min )

Open source Audio pipeline for transcription, translation and summarization. Check out our demo page to generate your own transcription, summary, and translation, or use our browser extension to get live transcriptions. submitted by /u/eusben [link] [comments]  ( 8 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Hey! I am working on a project to predict the best sequences of marketing channel so that sales is maximized. I have 20 ways of reaching out to the customer (email, phone, face2face...). I have 20 days of interaction history and it's generated sales, recorded for past 2 years. I have to predict for the next 20 working days(1 month) So far, I have tried ensemble methods, svm, fully connected nn, etc. But it is quite apparent that these are not good solutions. Any suggestions on ml/dl methods? Papers, blogs or other resources would be much appreciated submitted by /u/TUSH11235 [link] [comments]  ( 9 min )

submitted by /u/swodtke [link] [comments]  ( 8 min )

Greetings fellow researchers, I am 27, currently working remotely at a healthcare IT company based in Silicon Valley (6+ years in industrial research) where I apply deep learning methods and large language models. I recently received an exciting opportunity to pursue a PhD at the Technical University of Denmark (DTU) in a similar research area. While I am grateful for my current position and compensation, Have published in NeurIPS, EMNLP, ACL, ACM etc (NLP) with really good citations under company. I feel unsatisfied with the learning opportunities available in company & industry. I am strongly considering pursuing the DTU PhD program full-time, but wanted to get perspectives from others before making a decision. How strong is DTU's AI research community? Given the rapid advances in large language models, is now an ideal time to immerse myself in academic research? There are many topics that interest me, including fairness, ethics, hallucinations, quantization, specialized domains like healthcare/finance, and federated learning combined with LLMs. Would appreciate any insights on whether moving into academia would be a wise choice at this stage versus remaining in industry. I welcome any suggestions or considerations I should keep in mind. Thank you for taking the time to share your thoughts! submitted by /u/Traditional-Poet2746 [link] [comments]  ( 9 min )

Context I'm working on an RNN-based model that should learn how to guess the next character given a simple prompt based on all scripts from Friends to generate non-existing Friends dialogue. It is heavily inspired by Andrej Karpathy's blog post on RNN's. I'm mostly doing this for training, and because it's pretty fun. I have a little experience with deep learning in the sense that I am familiar with most common architectures and have intermediate understanding of how deep learning models work and are trained. I haven't created many models from scratch though, yet. Network My GRU is fairly simple. I'll save you the exact code, but instead give a systematic overview of all network layers. It's implemented with Pytorch: INPUT: sequence of integers representing a symbol based on mapping e…  ( 9 min )

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Hey. I'm currently working on an application that digitalizes text from physical book pages using Google's Cloud Vision API. I'm looking to add a functionality that can recognize and highlight underlined words within the scanned pages. I initially thought this would be a common feature and expected to find existing open-source solutions or libraries that I could use. To my surprise, I've been unable to find any. I am just really bad at finding it, or is this not as straightforward as I initially thought? submitted by /u/pangu2 [link] [comments]  ( 8 min )

Dear all, Here is Computer Vision News of August 2023. Read 44 pages about AI, Deep Learning, Computer Vision and more! Online version (recommended) PDF version Free subscription on page 44. Enjoy! https://preview.redd.it/e143wha20ggb1.jpg?width=794&format=pjpg&auto=webp&s=14a699f80f4b2de94addc8242e8978d3e185309f submitted by /u/Gletta [link] [comments]  ( 8 min )

Experts, I am a beginner here and seeking some advise here please. I am have compiled a high quality Q&A dataset (around 1200 entries) for a domain specific topic. What's the best course of action here to use LLM with that specific knowledge base? 1) Finetuning a model? if so which one is a good candidate? OpenAI let's me finetune some models and later, all my users have to do is use pass the model name to the API 2) Use the regular vector database + embeddings for augmented retrieval I prefer (1) but I am not sure how it will perform. Option (2) should work, since we really just use semantic search to bring in context to the LLM, etc. I hope you can say that (1) works nicely, if not please help me learn why. Thank you in advance! submitted by /u/entered_apprentice [link] [comments]  ( 9 min )

Where to find LLM related pytorch code with code explanations? submitted by /u/thorin_olamadal [link] [comments]  ( 8 min )

First time submitter here and was unable to find a similar post (and thought the community might benefit from this in the future!). How do I withdraw from Neurips? All the instructions I found are from 2017, 2018. Do I need to contact someone or do I just need to "Add Withdrawal" on OpenReview. submitted by /u/Dramatic-Gap-4681 [link] [comments]  ( 8 min )

Just what the title says. Also is this (splitting prediction into 2 stages) a prominent paradigm in other areas of ML too? I am reading about something called the "Action Transformer" created by Adept AI, and it also has 2 stages: instruction generation and code generation. submitted by /u/FloatingDelusion [link] [comments]  ( 8 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

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Web dev student here and I'm interested in knowing more about creating products that actually use AI to help its users (not products that just use GPT in the backend). More specifically, I want to build a food supply management app for restaurants for my school thesis. This app will use AI to analyse food supplies and assign them purchase priority, value, and complexity scores (maybe just priority if it's too hard). Restaurant owners could then determine what foods should be purchased before others based on the priority scores. For example, a restaurant may only have 10 tomatoes left and the average usage of tomatoes in this restaurant is 12 per week. Based on this, a priority would be assigned to purchase x amount of tomatoes. Other factors that could be taken into account for the prior…  ( 10 min )

Hello everyone Basically i just need to post some text into one website everyday for my work The problem is there are many steps involves to post one data value, I was wondering if there is a tool that can learn my tasks and then post some of the data to the website from google sheets? I'm open to any suggestions and advice. Thanks in advance. ​ submitted by /u/Maxduel [link] [comments]  ( 8 min )

I want to convert a few books into audiobooks. Are there any AI options out there that are free and will give me something I can use offline? I typically listen to books on my phone while I'm out, so something like Edge browser isn't going to work. I've heard that there are some great options, but I've only seen some web paid services, and for my purpose, it's too expensive just to get an audiobook out of it. This is all just for personal use. submitted by /u/UUkiee [link] [comments]  ( 8 min )

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https://imgur.com/WR0Tny9 My agent needs to learn to take one action in my environment and there are only two possible actions that the agent can take at each time step. The state is just the time step, so every episode has 240 time steps and the agent just needs to learn to take one optimal action out of two possible actions for every time step. I have set this up as simply as I can as a starting point to make sure the algorithm is implemented correctly and that the agent can learn. I am using n-step expected SARSA. The bottom plot shows the count for how many times the agent took each action during each episode. The middle plot has the temporal difference error in blue and the "modelling error" in orange. The modelling error is the difference between the actual discounted return and the TD target for each time step, summed up for each episode. The red line is the return that the agent would get if it took the optimal action in every time step. 0.11, the blue line in the bottom plot, is the optimal action for the agent to take at every time step. The other action will never result in a reward other than 0. So it should be fairly simple for the agent to learn what action to take at every time step and it does learn that at the start. But then, as you can see in the top plot, the agent suddenly starts taking the non-optimal action more often after around episode 450. So I'm just wondering why that would happen. Why would the agent learn to take the optimal action at most time steps and then suddenly decide that it will start taking other actions? For more context, the learning rate is 0.6, n is 6, epsilon is decayed by 1/(n_episodes/1.1) every episode so it reaches 0 slightly before the final episode. Any ideas based on this information why the agent would decide to start taking the non-optimal action? Or any suggestions for how I could figure out why it would start taking the non-optimal action? submitted by /u/lifelifebalance [link] [comments]  ( 9 min )

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Does anyone know code implementations for TarMAC: Targeted Multi-Agent Communication? submitted by /u/tessherelurkingnow [link] [comments]  ( 8 min )

The previous post looked at the swish function and related activation functions for deep neural networks designed to address the “dying ReLU problem.” Unlike many activation functions, the function f(x) is not monotone but has a minimum near x0 = -1.2784. The exact location of the minimum is where W is the Lambert W function, […] Swish function and a Swiss mathematician first appeared on John D. Cook.  ( 5 min )

Swish, mish, and serf are neural net activation functions. The names are fun to say, but more importantly the functions have been shown to improve neural network performance by solving the “dying ReLU problem.” Softplus can also be used as an activation function, but our interest in softplus here is as part of the definition […] Swish, mish, and serf first appeared on John D. Cook.  ( 7 min )

submitted by /u/AeroArtz [link] [comments]  ( 8 min )

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It’s incredible how many organizations utilize Generative AI (GenAI) and Large Language Models (LLMs) to enhance their information assembly, integration, and application abilities. These GenAI technologies have been applied in various areas, from drafting legal documents and resolving service issues to coding software applications and (er, um) writing blog posts. The potential uses of GenAI… Read More »Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part I The post Integrating GenAI into “Thinking Like a Data Scientist” Methodology – Part I appeared first on Data Science Central.  ( 23 min )

The movie that came out in the 80s is a great flick for it’s time. Do you guys think we will ever experience a sort of unstoppable super soldier when it comes to our police / swat forces ? We are replacing many jobs with robots. From surgery procedures in hospitals to flipping burgers. It’s not above the realm of possibility to think we may someday soon see a hybrid police force. What do you guys think ? submitted by /u/2bJavazon [link] [comments]  ( 8 min )

It's commonly used on tiktok for reddit narration story videos, here is an example: https://www.tiktok.com/@creekyadvice/video/7263509593488166186. Anyone have any idea? submitted by /u/DanielTube7 [link] [comments]  ( 8 min )

I am a linguist, translator, and copy editor looking to move my career into natural language processing instead. I have no computer science background. What would you suggest as some steps to take, both now and in the future, as I plan out my career? It looks like I am going to need to learn Python, but I'm not 100% sure, and there's so little established in such a new field. submitted by /u/StrangersWithAndi [link] [comments]  ( 8 min )

Interesting experiment i thought of. What if we gave AI access to web browser, and let it do whatever it wants? It could create accounts on any social media, email accounts, ad comments everywhere and such. of course, ai by itself does not have any agenda or need to do anything, so ai would need to be fed some kind of personality simulation first. Lets say ai was either fed personality based on extensive twitter or reddit history of someone's post. Using that, basic psychological traits, beliefs and maybe goals could be determined. Such ai would simulate person sitting in front of pc, so it would need to parse the content of webpages, but i don't think it would be that of a problem. And it would maybe also have access to some bank account with some money to maybe pay for online subscriptions and such. But who knows, maybe thanks to simulating someone's personality, it would attempt to donate money to some charity or lose it on onlyfans? submitted by /u/rogaldorn88888 [link] [comments]  ( 9 min )

I just published “Safe For Humans AI” – free to read online https://leanpub.com/safe-for-humans-AI/read Free to read online, and eBook versions released under a Creative Commons License (no commercial reuse, feel free to share). The full title of my short book is: Safe For Humans AI A "humans-first" approach to designing and building AI systems. submitted by /u/MWatson [link] [comments]  ( 8 min )

While some schools have curbed the use of generative AI, the University of Hong Kong (HKU) is going all in and urging both its teachers and students to embrace the technology. The University of Hong Kong is supporting this by giving teachers and students free access to various generative AI tools, including Microsoft Azure OpenAI and OpenAI’s ChatGPT and DALL-E.[1] Intel’s CEO, Pat Gelsinger, has called NVIDIA the clear market leader who has done a great job within the AI space.[2] AI powerhouse, OpenAI has released some new features for its sensational chatbot, ChatGPT. The new features allow the chatbot to show suggested follow-up prompts at the bottom of its responses. The new features were announced by the company via a tweet on its official Twitter handle.[3] Asian Americans and women in the workforce are the most concentrated in fields where AI could assist or replace their job tasks, according to new research.[4] BushAICave.com Sources: [1] https://www.zdnet.com/article/another-major-university-is-supporting-generative-ai-use-but-with-serious-guardrails/ [2] https://wccftech.com/intel-ceo-acknowledges-nvidia-as-ai-market-leader-says-they-have-done-a-good-job/ [3] https://indianexpress.com/article/technology/artificial-intelligence/chatgpt-gets-new-updates-heres-how-they-enhance-user-experience-8877847/ [4] https://www.nbcnews.com/news/asian-america/asian-american-workers-heavily-affected-ai-rcna98179 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

And if so, how has this account lasted 2 years on reddit? 🤔 submitted by /u/TheHeirOfElendil [link] [comments]  ( 8 min )

I’m a filmmaker and I’m just experimenting with AI. I just had fun crafting a film trailer to understand the today’s limits of these tools. I used Midjourney, Runway Gen-2, StableDiffusion, Premiere, After Effects. The movie it's called "The Phoenix", which hints at the film's underlying theme of rising from the ashes, symbolizing female empowerment, all wrapped in a bit of sarcastic humor from a male perspective. I'm sharing because I genuinely want to know what you guys think. Any and all thoughts are welcome. If you're curious about the workflow or the process behind the creation of this trailer, I'd be happy to share more. The Phoenix - She rises from the ashes submitted by /u/Lrnz_reddit [link] [comments]  ( 8 min )

Links in my bio for more content like this! submitted by /u/No_Understanding162 [link] [comments]  ( 8 min )

Hello there I'm curious what the user guidelines and restrictions are for the Adobe ai generative fill is and if there are possible better more higher quality and less restricted ones out there. submitted by /u/Team_Sonic_Gaming [link] [comments]  ( 8 min )

I'm not sure if this is the right subreddit to ask this on, but I'm creating this chatbot on dialogflow and I made the first intend, but I can't figure out how to enable it. whenever I test it, it shows the intend to be idf, and I can't just change the name of the intend to my current intend so it can recognize all the requests I've included in that intend. how do I do that? submitted by /u/penguinsandpandas00 [link] [comments]  ( 8 min )

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In principle you generate an RSA key by finding two large prime numbers, p and q, and computing n = pq. You could, for example, generate random numbers by rolling dice, then type the numbers into Mathematica to test each for primaility until you find a couple prime numbers of the right size. In practice […] Generating and inspecting an RSA private key first appeared on John D. Cook.  ( 6 min )

At its core, RSA encryption is modular exponentiation. That is, given a message m, the encrypted form of m is x = me mod n where e is a publicly known exponent and n is a product of two large primes. The number n is made public but only the holder of the private key […] RSA encryption in practice first appeared on John D. Cook.  ( 5 min )

A few days ago I wrote about using the CMU Pronouncing Dictionary to search for words that decode to certain numbers in the Major mnemonic system. You can find a brief description of the Major system in that post. As large as the CMU dictionary is, it did not contain words mapping to some three-digit […] Code to convert words to Major system numbers first appeared on John D. Cook.  ( 6 min )

Hello r/MachineLearning! I'm working on understanding how a neural network converges and wish to approach this mathematically. Can anyone recommend resources, papers, or tools that could assist me in proving this? Thank you in advance for your help! Edit: Removed converges faster to remove ambiguity submitted by /u/abystoma [link] [comments]  ( 8 min )

Do you aware of any work for realtime action recognition that use transformer? This is different with conventional transformer in a sense that we don’t have access to future information, so how do we change the training strategy? Also, it’s inefficient if we use the entire history; are there any smart way to select which frame in the past to keep? submitted by /u/Ok_Influence505 [link] [comments]  ( 8 min )

u/Vectara is now integrated with r/flowise, so you can easily build no-code GenAI Apps at scale. Check out the video here: https://twitter.com/ofermend/status/1687138158692196352 You can sign up for a free vectara.com account to get started. submitted by /u/ofermend [link] [comments]  ( 8 min )

I've recently started to explore the possibility of working with custom tokenizers. I will preface this by saying I'm not a tokenizer guy. I just don't know that much about their construction. I understand how they work, but I'm probably behind the latest developments in tokenizers. So, I thought it wisest to reach out to the community for advice or clarity. Context: I've collected about 15 GB of data over the last month. It's incredibly clean and well-organized. The core goal of the data is to train a model to solve or assist with a particular development problem. This means that much of my data is a code/natural language mix. It's delimited clearly, and the formatting is uniform. The entire dataset has been normalized and standardized. It's taken me a lot of time to produce and that's…  ( 10 min )

​ https://preview.redd.it/mbkb10icqbgb1.png?width=1400&format=png&auto=webp&s=7a15423060ddfeffe4651340bcc6fd7cf36dde10 I started a blog post series about the limitations of language models for dealing with long texts. Feedback is welcome! submitted by /u/JClub [link] [comments]  ( 8 min )

Hi everyone, With the recent boom of LLMs, we have seen both ends of the spectrum advance at a very fast pace, from OpenAI GPT4, which runs on huge data centers operated by Azure, to llama.cpp, which runs on consumer laptops. While both have their pros and cons, for instance, open-source models on decentralized compute reduce the need to trust or rely on centralized actors like Cloud providers, the efficiency of running training/inference on personal setups is not often discussed. I am therefore interested in learning how more energy/cost efficient it is to train/serve AI models on data centers vs doing it on personal computers. Do you know if there have been studies? In theory, I guess that several factors, such as economies of scale, use of renewable energy sources in some data centers, such as Canada, advanced cooling systems and advanced hardware, make data centers more cost/energy efficient. I guess some modeling on a precise use case where we fix some variables could help have an idea. For instance, one could ask, what is the energy/cost/time needed to predict 1 billion tokens from a Llama 2 70B in a data center with X amount of A100s, vs on Y different consumer CPU / GPUs. If anyone has references to models or past studies I would be quite interested. Of course, using data centers implies trusting those people, but I am not considering that factor for this discussion as I am focusing on understanding best what is the best setup to have optimal enrgy/cost/time for AI. submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

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As part of ICCV 2023 in Paris, this year we are organizing a challenge on solving domain gaps that occur when computer vision models are transferred across geographical locations. The challenge covers three tracks in unsupervised scene adaptation, image adaptation and universal adaptation. The challenge is open to everyone, with attractive prizes for the winners. Check it out at the following links! Challenge Rules and Guidelines: https://geonet-challenge.github.io/ICCV2023/challenge.html Challenge Registration: https://forms.gle/zSZA1iaPD3mZxjyn7 Code and baselines: https://github.com/ViLab-UCSD/GeoNet The training data for the challenge is already available, and the test data will be released to the registered participants. submitted by /u/GeoNetICCV2023 [link] [comments]  ( 8 min )

Hi guys Im looking for groups or communities where i could discuss about certain topic. Im a software developer and a mechanical engineer and recently made an app that combines gpt4 model to perform engineering tasks like CAD models creation and performing stress analysis. I would like find people who share the same passion and perhaps would like to discuss about that, exchange the concepts, ideas and visions. Im getting to the point where i will need to implement own trained model and im no ML expert so would be great to discuss about the architecture etc. Here is a demo of my app MechDesigner Assistant AI: Future Engineers Best regards Pyotr submitted by /u/pyotr_vozniak [link] [comments]  ( 9 min )

Hi, I am a new starter with ML apps and want to build a first app preferably using existing (trained) models. The idea is an app that takes a text description of a wished kick drum (for example: create a 808 kick with enhanced subs and filtered above 15kHz) and then generates a corresponding hifi sample of the description (44,1k or 48k). I would like to learn how to do that with some peers happy to help me. As said this would be my first attempt. About me: I only followed Deep Learning theoretical courses from Andrew Ng and never built or used existing models so I'd appreciate some guidance if you are interested to support. Thanks a lot submitted by /u/freeabt19 [link] [comments]  ( 9 min )

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Popular machine model techniques such as LightGBM and XGBoost output predictions that are unimodally distributed(only one hump) but seem to beat other models specialized to deal with multimodal data. Or am I just wrong? It just doesnt look right. https://preview.redd.it/6xrd7hgm4agb1.png?width=1000&format=png&auto=webp&s=a4518549f609c6436af410ae87a0c6a24cff6ea7 submitted by /u/runawaychicken [link] [comments]  ( 8 min )

Hey I've tried to implement the transformer architecture on my own to understand it better. The outputs look fine (I'm only looking at shapes) and I wanted to know if it's right firstly, and if there is anyway to implement it in a more efficient way. Code - import torch import torch.nn as nn class MultiHeadSelfAttention(nn.Module): def __init__(self, nheads=8, dim=512, bias=True, dropout=0.2): super().__init__() assert dim % nheads == 0, "dimension must be divisible by number of heads" self.nheads = nheads self.dim = dim self.head_dim = dim // nheads self.scale = self.head_dim**-0.5 self.softmax = nn.Softmax(dim=-1) self.dropout = nn.Dropout(dropout) self.to_keys = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_queries = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_values = nn.Linear(dim, self.dim_heads * nheads, bias=bias) self.to_out = nn.Linear(self.dim_heads * nheads, dim, bias=bias) def change_shape(self, x): b_size = x.shape[:-1] return x.reshape(*b_size, self.nheads, self.head_dim) def forward(self, x, mask=True): q = self.change_shape(self.to_queries(x)) k = self.change_shape(self.to_keys(x)) v = self.change_shape(self.to_values(x)) dot_score = q @ k.transpose(-2, -1) * self.scale if mask: tril = torch.tril(torch.ones(dot_score.shape[-2:])) dot_score = dot_score.masked_fill(tril == 0, float("-inf")) attn = self.softmax(attn) attn = self.dropout(attn) out = torch.einsum("bnk,bnd->bnd", attn, v) b_size = out.shape[:-2] out = out.view(*b_size, -1) return self.to_out(out) Thank you! submitted by /u/04RR [link] [comments]  ( 9 min )

https://github.com/NoteDancing/Note This project allows you to easily implement parallel training with the multiprocessing module. submitted by /u/NoteDancing [link] [comments]  ( 8 min )

In the forward process of diffusion models, gaussian noise is added -- when this is done, is the resulting "noisy image" clipped to be within the pixel-value bounds (ie [0, 255] or [0, 1]), or is it allowed to exceed these limits? Clipping makes sense as there is no interpretation for pixel values which exceed these limits. On the other hand, the problem with clipping is that if the added noise is clipped, you are not adding truly gaussian noise, which seems problematic as much of the theory behind diffusion models assumes true gaussian noise. Any ideas about what is done in practice, and whether or not this has implications from a theoretical standpoint? submitted by /u/alkaway [link] [comments]  ( 9 min )

Good Evening ML peeps So I am currently creating a dataset in a team of three. This dataset is aimed to create a object detection model for around 11 classes. We have aimed to label around approx. 4000. Our current workflow is a couple of scripts scraping from Pinterest and using Label Studio for labeling. We labeled approx. 25% to our goal but realized that we are about to burn out. We'd prefer that whatever solution there is is self hosted and not paid. Thoughts? is there some kind of workflow we are missing to create a dataset? submitted by /u/PlanetAcorn [link] [comments]  ( 9 min )

I am working on a project that is very popular with the inception of Langchain + GPT applications. However, I want to make it open source and hence don't want to use GPT. So something like Langchain + LLama2, etc. I know currently Langchain only supports GPT but any other ideas are highly appreciated! submitted by /u/vishank97 [link] [comments]  ( 8 min )

Hi all, I'm interested in redesigning my application to utilize an open-source embeddings model and a different vector DB. My current issue with embeddings is that processing large volumes of data into a vector DB using ada-002 is unreliable, with frequent API timeouts occurring or issues interacting with Pinecone. This is super problematic as it's difficult to track which data has / hasn't been stored correctly. I also know that many open-source embeddings models are more performant and will allow for more long term control over my data. However, the advantage of using OpenAI / Pinecone has of course been simplicity in production and not having to worry about queries / retrieval working efficiently. To give context, I'm dealing with a large volume of documents, such that if I were to embed my documents into a FAISS index with a small sentence transformers model, it would constitute 12GB, so a really simple solution like storing within the same application database is probably a no-go. In initiating this switch, I want to know the best approach towards: A) Utilizing an open-source embeddings model in a production context (is it best to host as an API via a cloud provider and what are some considerations I should think about? What's a fast / reliable way of setting this up? I would like prioritise a more simple approach if possible.) B) What Vector DB I should be looking into as an alternative and what's the best way to achieve self-hosted so that it would be equally performant compared to hosted services like pinecone (Docker? AWS?)? submitted by /u/theheffalump2000 [link] [comments]  ( 9 min )

SARSA is a TD algorithm for control (learning optimal policies). In the book it's written like this: image. The idea is to learn the action-value function instead of the value function for a policy that we keep improving (using GPI). Once we learn the converged action-value function for all states, the optimal policy is greedily derived from the action-value function (basically take the most promising action at each state). In contrast, TD for value estimation is written like this: image. Here we keep the policy fixed and just keep iterating over the multiple episodes, whilst refining the value estimate. My question is, why can't we just change TD for value estimation to just greedily update the policy at each stage? That would be in the spirit of generalized policy iteration (GPI) too. In other words, a version of SARSA which doesn't use action-value functions, but instead use value functions? submitted by /u/AstronautVarious3791 [link] [comments]  ( 9 min )

Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper presents an unsupervised machine learning algorithm to extract defining characteristics of earthquake ground-motion spectra, also called latent features, to aid in ground-motion selection (GMS). In this context, a latent feature is a low-dimensional machine-discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Machine discovered latent features can be combined with traditionally defined intensity measures and clustering can be performed to select a representative subgroup from a large ground-motion suite. The objective of efficient GMS is to choose characteristic records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including the use of synthetic and field recorded ground-motion datasets. The presented deep embedding clustering of ground-motion spectra has three main advantages: 1. defining characteristics the represent the sparse spectral content of ground-motions are discovered efficiently through training of the autoencoder, 2. domain knowledge is incorporated into the machine learning framework with conditional variables in the deep embedding scheme, and 3. method exhibits excellent performance when compared to a benchmark seismic hazard analysis.  ( 2 min )

(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic system models that are sufficiently accurate in all relevant state-space regions. These models must also be computationally cheap enough to ensure real-time tractability. Data-driven surrogate models for mechanistic models can be used to reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum average prediction accuracy on simulation samples and perform suboptimally as part of actual (e)NMPC. We present a method for end-to-end reinforcement learning of dynamic surrogate models for optimal performance in (e)NMPC applications, resulting in predictive controllers that strike a favorable balance between control performance and computational demand. We validate our method on two applications derived from an established nonlinear continuous stirred-tank reactor model. We compare the controller performance to that of MPCs utilizing models trained by the prevailing maximum prediction accuracy paradigm, and model-free neural network controllers trained using reinforcement learning. We show that our method matches the performance of the model-free neural network controllers while consistently outperforming models derived from system identification. Additionally, we show that the MPC policies can react to changes in the control setting without retraining.  ( 2 min )

Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data. Till now, rehearsal-based methods, keeping a small part of data from old tasks as a memory buffer, have shown good performance in mitigating catastrophic forgetting for previously learned knowledge. However, most of these methods typically treat each new task equally, which may not adequately consider the relationship or similarity between old and new tasks. Furthermore, these methods commonly neglect sample importance in the continual training process and result in sub-optimal performance on certain tasks. To address this challenging problem, we propose Relational Experience Replay (RER), a bi-level learning framework, to adaptively tune task-wise relationships and sample importance within each task to achieve a better `stability' and `plasticity' trade-off. As such, the proposed method is capable of accumulating new knowledge while consolidating previously learned old knowledge during continual learning. Extensive experiments conducted on three publicly available datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) show that the proposed method can consistently improve the performance of all baselines and surpass current state-of-the-art methods.  ( 2 min )

Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs. However, previous methods usually overlook the issues in practical applications, i.e., the out-of-sample issue and the noise issue. To address the above issues, in this paper, we propose an effective and efficient UMGL method to explore both complementary and consistent information. To do this, our method employs multiple MLP encoders rather than graph convolutional network (GCN) to conduct representation learning with two constraints, i.e., preserving the local graph structure among nodes to handle the out-of-sample issue, and maximizing the correlation of multiple node representations to handle the noise issue. Comprehensive experiments demonstrate that our proposed method achieves superior effectiveness and efficiency over the comparison methods and effectively tackles those two issues. Code is available at https://github.com/LarryUESTC/CoCoMG.  ( 2 min )

We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models. On seven vision-language datasets, OpenFlamingo models average between 80 - 89% of corresponding Flamingo performance. This technical report describes our models, training data, hyperparameters, and evaluation suite. We share our models and code at https://github.com/mlfoundations/open_flamingo.  ( 2 min )

The development of technologies for causal inference with the privacy preservation of distributed data has attracted considerable attention in recent years. To address this issue, we propose a data collaboration quasi-experiment (DC-QE) that enables causal inference from distributed data with privacy preservation. In our method, first, local parties construct dimensionality-reduced intermediate representations from the private data. Second, they share intermediate representations, instead of private data for privacy preservation. Third, propensity scores were estimated from the shared intermediate representations. Finally, the treatment effects were estimated from propensity scores. Our method can reduce both random errors and biases, whereas existing methods can only reduce random errors in the estimation of treatment effects. Through numerical experiments on both artificial and real-world data, we confirmed that our method can lead to better estimation results than individual analyses. Dimensionality-reduction loses some of the information in the private data and causes performance degradation. However, we observed that in the experiments, sharing intermediate representations with many parties to resolve the lack of subjects and covariates, our method improved performance enough to overcome the degradation caused by dimensionality-reduction. With the spread of our method, intermediate representations can be published as open data to help researchers find causalities and accumulated as a knowledge base.  ( 2 min )

Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms and their comparative analysis. The weakness of one learning algorithm turns out to be a strength of another, and thus merging them is a prevalent trait in the literature. There are numerous research papers that focus on each of these learning paradigms separately and provide a comprehensive overview of them. However, this article provides a review of research studies that combine (two of) these learning algorithms. This survey describes how these techniques are combined to solve problems in many different fields of study, including computer vision, natural language processing, hyperspectral imaging, and many more, in supervised setting only. As a result, the global generic learning network an amalgamation of meta learning, transfer learning, and multi-task learning is introduced here, along with some open research questions and future research directions in the multi-task setting.  ( 3 min )

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better ${\bf visual}$ dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification.  ( 3 min )

Energy theft detection (ETD) and energy consumption forecasting (ECF) are two interconnected challenges in smart grid systems. Addressing these issues collectively is crucial for ensuring system security. This paper addresses the interconnected challenges of ETD and ECF in smart grid systems. The proposed solution combines long short-term memory (LSTM) and a denoising diffusion probabilistic model (DDPM) to generate input reconstruction and forecasting. By leveraging the reconstruction and forecasting errors, the system identifies instances of energy theft, with the methods based on reconstruction error and forecasting error complementing each other in detecting different types of attacks. Through extensive experiments on real-world and synthetic datasets, the proposed scheme outperforms baseline methods in ETD and ECF problems. The ensemble method significantly enhances ETD performance, accurately detecting energy theft attacks that baseline methods fail to detect. The research offers a comprehensive and effective solution for addressing ETD and ECF challenges, demonstrating promising results and improved security in smart grid systems.  ( 2 min )

Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temporally extended exploration, i.e., deep exploration, we focus on deep exploration in distributional RL. We develop here a general purpose approach, Bag of Policies (BoP), that can be built on top of any return distribution estimator by maintaining a population of its copies. BoP consists of an ensemble of multiple heads that are updated independently. During training, each episode is controlled by only one of the heads and the collected state-action pairs are used to update all heads off-policy, leading to distinct learning signals for each head which diversify learning and behaviour. To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e.g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG). The population thus approximates a posterior distribution of return distributions along with a posterior distribution of policies. Another benefit of building upon BDPG is that it allows to analyze global posterior uncertainty along with local curiosity bonus simultaneously for exploration. As BDPG is already an optimistic method, this pairing helps to investigate if optimism is accumulatable in distributional RL. Overall BoP results in greater robustness and speed during learning as demonstrated by our experimental results on ALE Atari games.  ( 2 min )

Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time series data, due to its unique annotation bottleneck caused by its complex characteristics and lack of visual cues compared with other data modalities. In recent years, unsupervised representation learning techniques have advanced rapidly in various domains. However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series. To fill the gap, we conduct a comprehensive literature review of existing rapidly evolving unsupervised representation learning approaches for time series. Moreover, we also develop a unified and standardized library, named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast implementations and unified evaluations on various models. With ULTS, we empirically evaluate state-of-the-art approaches, especially the rapidly evolving contrastive learning methods, on 9 diverse real-world datasets. We further discuss practical considerations as well as open research challenges on unsupervised representation learning for time series to facilitate future research in this field.

Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant clinical interest. They play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D parametric maps conventionally used for clinical interpretation by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In addition, these methods are only interested in segmenting core regions, while predicting penumbra can be essential for treatment planning. This paper investigates different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information, leading us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for instance Concrete dropout (Gal et al., 2017) and Deep Ensembles (Lakshminarayanan et al., 2017). Specifically, an MVE network assumes that the data is produced from a normal distribution with a mean function and variance function. The MVE network outputs a mean and variance estimate and optimizes the network parameters by minimizing the negative loglikelihood. In our paper, we present two significant insights. Firstly, the convergence difficulties reported in recent work can be relatively easily prevented by following the simple yet often overlooked recommendation from the original authors that a warm-up period should be used. During this period, only the mean is optimized with a fixed variance. We demonstrate the effectiveness of this step through experimentation, highlighting that it should be standard practice. As a sidenote, we examine whether, after the warm-up, it is beneficial to fix the mean while optimizing the variance or to optimize both simultaneously. Here, we do not observe a substantial difference. Secondly, we introduce a novel improvement of the MVE network: separate regularization of the mean and the variance estimate. We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.

Text-to-image diffusion models such as Stable Diffusion have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process and how to engineer prompts in order to obtain the desired images. To this end, we introduce the new task of predicting the text prompt given an image generated by a generative diffusion model. We combine a series of white-box and black-box models (with and without access to the weights of the diffusion network) to deal with the proposed task. We propose a novel learning framework comprising of a joint prompt regression and multi-label vocabulary classification objective that generates improved prompts. To further improve our method, we employ a curriculum learning procedure that promotes the learning of image-prompt pairs with lower labeling noise (i.e. that are better aligned), and an unsupervised domain-adaptive kernel learning method that uses the similarities between samples in the source and target domains as extra features. We conduct experiments on the DiffusionDB data set, predicting text prompts from images generated by Stable Diffusion. Our novel learning framework produces excellent results on the aforementioned task, yielding the highest gains when applied on the white-box model. In addition, we make an interesting discovery: training a diffusion model on the prompt generation task can make the model generate images that are much better aligned with the input prompts, when the model is directly reused for text-to-image generation.

Stochastic gradient descent (SGD) is the simplest deep learning optimizer with which to train deep neural networks. While SGD can use various learning rates, such as constant or diminishing rates, the previous numerical results showed that SGD performs better than other deep learning optimizers using when it uses learning rates given by line search methods. In this paper, we perform a convergence analysis on SGD with a learning rate given by an Armijo line search for nonconvex optimization. The analysis indicates that the upper bound of the expectation of the squared norm of the full gradient becomes small when the number of steps and the batch size are large. Next, we show that, for SGD with the Armijo-line-search learning rate, the number of steps needed for nonconvex optimization is a monotone decreasing convex function of the batch size; that is, the number of steps needed for nonconvex optimization decreases as the batch size increases. Furthermore, we show that the stochastic first-order oracle (SFO) complexity, which is the stochastic gradient computation cost, is a convex function of the batch size; that is, there exists a critical batch size that minimizes the SFO complexity. Finally, we provide numerical results that support our theoretical results. The numerical results indicate that the number of steps needed for training deep neural networks decreases as the batch size increases and that there exist the critical batch sizes that can be estimated from the theoretical results.

Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches all involve approximations of some kind, including the use of surrogates for the 0-1 loss (for example, the hinge or logistic loss) or approximate combinatorial search, none of which can be guaranteed to solve the problem exactly. Finding efficient algorithms to obtain an exact i.e. globally optimal solution for the 0-1 loss linear classification problem with fixed dimension, remains an open problem. In research we report here, we detail the rigorous construction of a new algorithm, incremental cell enumeration (ICE), that can solve the 0-1 loss classification problem exactly in polynomial time. We prove correctness using concepts from the theory of hyperplane arrangements and oriented matroids. We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time. We also empirically demonstrate how the use of approximate upper bound leads to polynomial time run-time improvements to the algorithm whilst retaining exactness. To our knowledge, this is the first, rigorously-proven polynomial time, practical algorithm for this long-standing problem.

We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to lower order interaction terms. The idea is to modify the random forest algorithm by keeping certain leaves after they are split instead of deleting them. This leads to non-binary trees which we refer to as planted trees. An extension to a forest leads to our random planted forest algorithm. Additionally, the maximum number of covariates which can interact within a leaf can be bounded. If we set this interaction bound to one, the resulting estimator is a sum of one-dimensional functions. In the other extreme case, if we do not set a limit, the resulting estimator and corresponding model place no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealized version of random planted forests in cases where the interaction bound is low. We show that if it is smaller than three, the idealized version achieves asymptotically optimal convergence rates up to a logarithmic factor. Code is available on GitHub https://github.com/PlantedML/randomPlantedForest.

DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets and jointly identify common patterns in DNA methylation. MIRACLE's architecture reflects the relationships between methylation sites, genes, and pathways, ensuring biological interpretability and meaningfulness. The network comprises an encoder and a decoder, with a bottleneck layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-gene-pathway hierarchical structure explicitly. And from the embedding, there are different multi-task classifiers to predict diseases. Tested on six datasets, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases.

In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. When combined with a sufficiently fast data-structure for (perhaps approximate) adaptive nearest neighbour search, such as a navigating net, our algorithm is extremely efficient - having a per trial running time polylogarithmic in both the number of trials and actions, and taking only quasi-linear space.

Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network. Our experiments demonstrate that VertexSerum significantly outperforms the SOTA link inference attack, improving the AUC scores by an average of $9.8\%$ across four real-world datasets and three different GNN structures. Furthermore, our experiments reveal the effectiveness of VertexSerum in both black-box and online learning settings, further validating its applicability in real-world scenarios.

Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Our results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes.

How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, long-tail class incremental learning and few-shot class incremental learning are also proposed to consider the data imbalance and data scarcity, respectively, which are common in real-world implementations and further exacerbate the well-known problem of catastrophic forgetting. Existing methods are specifically proposed for one of the three tasks. In this paper, we offer a unified solution to the misalignment dilemma in the three tasks. Concretely, we propose neural collapse terminus that is a fixed structure with the maximal equiangular inter-class separation for the whole label space. It serves as a consistent target throughout the incremental training to avoid dividing the feature space incrementally. For CIL and LTCIL, we further propose a prototype evolving scheme to drive the backbone features into our neural collapse terminus smoothly. Our method also works for FSCIL with only minor adaptations. Theoretical analysis indicates that our method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity. We also design a generalized case where we do not know the total number of classes and whether the data distribution is normal, long-tail, or few-shot for each coming session, to test the generalizability of our method. Extensive experiments with multiple datasets are conducted to demonstrate the effectiveness of our unified solution to all the three tasks and the generalized case.

Multitask learning is a powerful framework that enables one to simultaneously learn multiple related tasks by sharing information between them. Quantifying uncertainty in the estimated tasks is of pivotal importance for many downstream applications, such as online or active learning. In this work, we provide novel multitask confidence intervals in the challenging agnostic setting, i.e., when neither the similarity between tasks nor the tasks' features are available to the learner. The obtained intervals do not require i.i.d. data and can be directly applied to bound the regret in online learning. Through a refined analysis of the multitask information gain, we obtain new regret guarantees that, depending on a task similarity parameter, can significantly improve over treating tasks independently. We further propose a novel online learning algorithm that achieves such improved regret without knowing this parameter in advance, i.e., automatically adapting to task similarity. As a second key application of our results, we introduce a novel multitask active learning setup where several tasks must be simultaneously optimized, but only one of them can be queried for feedback by the learner at each round. For this problem, we design a no-regret algorithm that uses our confidence intervals to decide which task should be queried. Finally, we empirically validate our bounds and algorithms on synthetic and real-world (drug discovery) data.

With the widespread application of personalized online services, click-through rate (CTR) prediction has received more and more attention and research. The most prominent features of CTR prediction are its multi-field categorical data format, and vast and daily-growing data volume. The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances. The self-supervised learning paradigm provides a more promising pretrain-finetune solution to better exploit the large amount of user click logs, and learn more generalized and effective representations. However, self-supervised learning for CTR prediction is still an open question, since current works on this line are only preliminary and rudimentary. To this end, we propose a Model-agnostic pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). MFP digs into feature interactions within each instance through masking and predicting a small portion of input features, and introduces noise contrastive estimation (NCE) to handle large feature spaces. RFD further turns MFP into a binary classification mode through replacing and detecting changes in input features, making it even simpler and more effective for CTR pretraining. Our extensive experiments on two real-world large-scale datasets (i.e., Avazu, Criteo) demonstrate the advantages of these two methods on several strong backbones (e.g., DCNv2, DeepFM), and achieve new state-of-the-art performance in terms of both effectiveness and efficiency for CTR prediction.

Diffusion models have emerged as the \emph{de-facto} technique for image generation, yet they entail significant computational overhead, hindering the technique's broader application in the research community. We propose a prior-based denoising training framework, the first to incorporate the pre-train and fine-tune paradigm into the diffusion model training process, which substantially improves training efficiency and shows potential in facilitating various downstream tasks. Our approach centers on masking a high proportion (e.g., up to 90\%) of the input image and employing masked denoising score matching to denoise the visible areas, thereby guiding the diffusion model to learn more salient features from training data as prior knowledge. By utilizing masked learning in a pre-training stage, we efficiently train the ViT-based diffusion model on CelebA-HQ $256 \times 256$ in the pixel space, achieving a 4x acceleration and enhancing the quality of generated images compared to denoising diffusion probabilistic model (DDPM). Moreover, our masked pre-training technique can be universally applied to various diffusion models that directly generate images in the pixel space, aiding in the learning of pre-trained models with superior generalizability. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement through fine-tuning with merely 10\% data from a different distribution. Moreover, our method shows the potential to serve as a training paradigm for enhancing the privacy protection capabilities of diffusion models. Our code is available at \url{https://github.com/jiachenlei/maskdm}.

We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. Moreover, our results consistently indicate that training with the Quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly-used mean squared error. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.

We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One of the standard approaches for solving this problem is to reformulate it as an optimization problem: finding the boundary of the domain that minimizes the $L^2$ distance between computed values of the scattered field and the given measurement data. The optimization problem is computationally challenging since the local set of convexity shrinks with increasing frequency and results in an increasing number of local minima in the vicinity of the true solution. In many practical experimental settings, low frequency measurements are unavailable due to limitations of the experimental setup or the sensors used for measurement. Thus, obtaining a good initial guess for the optimization problem plays a vital role in this environment. We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network. We demonstrate the effectiveness of our method with several numerical examples. For high frequency problems, this approach outperforms traditional iterative methods such as Gauss-Newton initialized without any prior (i.e., initialized using a unit circle), or initialized using the solution of a direct method such as the linear sampling method. The algorithm remains robust to noise in the scattered field measurements and also converges to the true solution for limited aperture data. However, the number of training samples required to train the neural network scales exponentially in frequency and the complexity of the obstacles considered. We conclude with a discussion of this phenomenon and potential directions for future research.

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the Combined Actuarial Neural Network (CANN) framework proposed by Mario W\"uthrich and Michael Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial (MVNB) specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examining emerging families need to be explored. This paper focuses on the online processing of incoming malicious samples to assign them to existing families or, in the case of samples from new families, to cluster them. We experimented with seven prevalent malware families from the EMBER dataset, four in the training set and three additional new families in the test set. Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families. We classified 97.21% of streaming data with a balanced accuracy of 95.33%. Then, we clustered the remaining data using a self-organizing map, achieving a purity from 47.61% for four clusters to 77.68% for ten clusters. These results indicate that our approach has the potential to be applied to the classification and clustering of zero-day malware into malware families.

The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low emission levels. Due to the wide range of design and operating parameters for large gas engine prechambers, the preferred method for evaluating different designs is computational fluid dynamics (CFD), as testing in test bed measurement campaigns is time-consuming and expensive. However, the significant computational time required for detailed CFD simulations due to the complexity of solving the underlying physics also limits its applicability. In optimization settings similar to the present case, i.e., where the evaluation of the objective function(s) is computationally costly, Bayesian optimization has largely replaced classical design-of-experiment. Thus, the present study deals with the computationally efficient Bayesian optimization of large gas engine prechambers design using CFD simulation. Reynolds-averaged-Navier-Stokes simulations are used to determine the target values as a function of the selected prechamber design parameters. The results indicate that the chosen strategy is effective to find a prechamber design that achieves the desired target values.

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former poses a safety concern as the number of detections needs to be kept low for efficiency reasons, sacrificing object recall. The latter is computationally expensive due to the high-dimensionality of the output grid, and suffers from the limited receptive field inherent to fully convolutional networks. Furthermore, both approaches employ many computational resources predicting areas or objects that might never be queried by the motion planner. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network. Our method avoids unnecessary computation, as it can be directly queried by the motion planner at continuous spatio-temporal locations. Moreover, we design an architecture that overcomes the limited receptive field of previous explicit occupancy prediction methods by adding an efficient yet effective global attention mechanism. Through extensive experiments in both urban and highway settings, we demonstrate that our implicit model outperforms the current state-of-the-art. For more information, visit the project website: https://waabi.ai/research/implicito.

With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimation methods is Deep Ensembles (Lakshminarayanan et al., 2017) . A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample. A modern neural network has an additional uncertainty component since the optimization of the network is random. Lakshminarayanan et al. (2017) noted that Deep Ensembles do not incorporate the classical uncertainty induced by the effect of finite data. In this paper, we present a computationally cheap extension of Deep Ensembles for the regression setting, called Bootstrapped Deep Ensembles, that explicitly takes this classical effect of finite data into account using a modified version of the parametric bootstrap. We demonstrate through an experimental study that our method significantly improves upon standard Deep Ensembles

While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law which possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types -- such as images and time-series data (e.g., audio or text data) -- requires a deep metric learning loss that minimizes the distance between the modality embeddings. In this paper, we propose to use the contrastive or triplet loss, which uses positive and negative identities to create sample pairs with different labels, for cross-modal representation learning between image and time-series modalities (CMR-IS). By adapting the triplet loss for cross-modal representation learning, higher accuracy in the main (time-series classification) task can be achieved by exploiting additional information of the auxiliary (image classification) task. We present a triplet loss with a dynamic margin for single label and sequence-to-sequence classification tasks. We perform extensive evaluations on synthetic image and time-series data, and on data for offline handwriting recognition (HWR) and on online HWR from sensor-enhanced pens for classifying written words. Our experiments show an improved classification accuracy, faster convergence, and better generalizability due to an improved cross-modal representation. Furthermore, the more suitable generalizability leads to a better adaptability between writers for online HWR.

We present a latent variable generalisation of neural network softmax classification trained with cross-entropy loss, referred to as variational classification (VC). Our approach offers a novel probabilistic perspective on the highly familiar softmax classification model, to which it relates similarly to how variational and traditional autoencoders relate. We derive a training objective based on the evidence lower bound (ELBO) that is non-trivial to optimize, and therefore propose an adversarial approach to maximise it. We show that VC addresses an inherent inconsistency within softmax classification, whilst also allowing more flexible choices of prior distributions in the latent space in place of implicit assumptions revealed within off-the-shelf softmax classifiers. Empirical evaluation on image and text classification datasets demonstrates that variational classification maintains prediction accuracy while improving other desirable properties such as calibration and adversarial robustness, particularly under distribution shift and low data settings.

Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learning framework called MARLIM, to address the inventory management problem for a single-echelon multi-products supply chain with stochastic demands and lead-times. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area.

For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of correctness for subsequent decision-making. Existing dense binary classification models are prone to being over-confident. To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image. ASLP employs our proposed Self-Calibrating Binary Cross Entropy (SC-BCE) loss, which unifies label perturbation processes including stochastic approaches (like DisturbLabel), and label smoothing, to correct calibration while maintaining classification rates. ASLP follows Maximum Entropy Inference of classic statistical mechanics to maximise prediction entropy with respect to missing information. It performs this while: (1) preserving classification accuracy on known data as a conservative solution, or (2) specifically improves model calibration degree by minimising the gap between the prediction accuracy and expected confidence of the target training label. Extensive results demonstrate that ASLP can significantly improve calibration degrees of dense binary classification models on both in-distribution and out-of-distribution data. The code is available on https://github.com/Carlisle-Liu/ASLP.

Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate. In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image? We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images. We show how such generative concepts can accurately represent the content of images, be recombined and composed to generate new artistic and hybrid images, and be further used as a representation for downstream classification tasks.

Diffusion models and large language models have emerged as leading-edge generative models and have sparked a revolutionary impact on various aspects of human life. However, the practical implementation of these models has also exposed inherent risks, highlighting their dual nature and raising concerns regarding their trustworthiness. Despite the abundance of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, This paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: privacy, security, fairness, and responsibility. In this way, we construct an extensive map outlining the trustworthiness of these models, while also providing practical recommendations and identifying future directions. These efforts are crucial for promoting the trustworthy deployment of these models, ultimately benefiting society as a whole.

We deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost over a time horizon $T$ while guarantees the security of the transmitted information. For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time-varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in $\mathcal{O}( \sqrt{T} )$ when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerical experiment results on both synthetic and real-world datasets verify the effectiveness of our algorithms.

Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simulation (LES) has been widely used as an alternative to direct numerical simulation (DNS) for simulating turbulent flows due to its reduced computational cost. However, LES is unable to capture all of the scales of turbulent transport accurately. Reconstructing DNS from low-resolution LES is critical for many scientific and engineering disciplines, but it poses many challenges to existing super-resolution methods due to the spatio-temporal complexity of turbulent flows. In this work, we propose a new physics-guided neural network for reconstructing the sequential DNS from low-resolution LES data. The proposed method leverages the partial differential equation that underlies the flow dynamics in the design of spatio-temporal model architecture. A degradation-based refinement method is also developed to enforce physical constraints and further reduce the accumulated reconstruction errors over long periods. The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.

The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morphological classification of extragalactic radio sources have proposed classifiers based on convolutional neural networks. Alternatively, this work proposes gradient boosting machine learning methods accompanied by principal component analysis as data-efficient alternatives to convolutional neural networks. Recent findings have shown the efficacy of gradient boosting methods in outperforming deep learning methods for classification problems with tabular data. The gradient boosting methods considered in this work are based on the XGBoost, LightGBM, and CatBoost implementations. This work also studies the effect of dataset size on classifier performance. A three-class classification problem is considered in this work based on the three main Fanaroff-Riley classes: class 0, class I, and class II, using radio sources from the Best-Heckman sample. All three proposed gradient boosting methods outperformed a state-of-the-art convolutional neural networks-based classifier using less than a quarter of the number of images, with CatBoost having the highest accuracy. This was mainly due to the superior accuracy of gradient boosting methods in classifying Fanaroff-Riley class II sources, with 3$\unicode{x2013}$4% higher recall.

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process.

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.

Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low computational costs. Nonetheless, we discover a critical issue of single-path DARTS that has not been primarily noticed. Namely, it also suffers from severe performance collapse since too many parameter-free operations like skip connections are derived, just like DARTS does. In this paper, we propose a new algorithm called RObustifying Memory-Efficient NAS (ROME) to give a cure. First, we disentangle the topology search from the operation search to make searching and evaluation consistent. We then adopt Gumbel-Top2 reparameterization and gradient accumulation to robustify the unwieldy bi-level optimization. We verify ROME extensively across 15 benchmarks to demonstrate its effectiveness and robustness.

We consider the degree-Rips construction from topological data analysis, which provides a density-sensitive, multiparameter hierarchical clustering algorithm. We analyze its stability to perturbations of the input data using the correspondence-interleaving distance, a metric for hierarchical clusterings that we introduce. Taking certain one-parameter slices of degree-Rips recovers well-known methods for density-based clustering, but we show that these methods are unstable. However, we prove that degree-Rips, as a multiparameter object, is stable, and we propose an alternative approach for taking slices of degree-Rips, which yields a one-parameter hierarchical clustering algorithm with better stability properties. We prove that this algorithm is consistent, using the correspondence-interleaving distance. We provide an algorithm for extracting a single clustering from one-parameter hierarchical clusterings, which is stable with respect to the correspondence-interleaving distance. And, we integrate these methods into a pipeline for density-based clustering, which we call Persistable. Adapting tools from multiparameter persistent homology, we propose visualization tools that guide the selection of all parameters of the pipeline. We demonstrate Persistable on benchmark datasets, showing that it identifies multi-scale cluster structure in data.

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.

Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the nature of time-series data, e.g, channel-independence (CI) and channel-dependence (CD), rather than solely focusing on designing sophisticated forecasting models. However, current research has primarily focused on either CI or CD in isolation, and the challenge of effectively combining these two opposing properties to achieve a synergistic effect remains an unresolved issue. In this paper, we carefully examine the opposing properties of CI and CD, and raise a practical question that has not been effectively answered, e.g.,"How to effectively mix the CI and CD properties of time series to achieve better predictive performance?" To answer this question, we propose Mlinear (MIX-Linear), a simple yet effective method based mainly on linear layers. The design philosophy of Mlinear mainly includes two aspects:(1) dynamically tuning the CI and CD properties based on the time semantics of different input time series, and (2) providing deep supervision to adjust the individual performance of the "CI predictor" and "CD predictor". In addition, empirically, we introduce a new loss function that significantly outperforms the widely used mean squared error (MSE) on multiple datasets. Experiments on time-series datasets covering multiple fields and widely used have demonstrated the superiority of our method over PatchTST which is the lateset Transformer-based method in terms of the MSE and MAE metrics on 7 datasets with identical sequence inputs (336 or 512). Specifically, our method significantly outperforms PatchTST with a ratio of 21:3 at 336 sequence length input and 29:10 at 512 sequence length input. Additionally, our approach has a 10 $\times$ efficiency advantage at the unit level, taking into account both training and inference times.

While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that clearly demonstrates the impact of the model pruning and wireless communications between the clients and the associated BS. Then we jointly optimize the model pruning ratio, central processing unit (CPU) frequency and transmission power of the clients in order to minimize the controllable terms of the convergence bound under strict delay and energy constraints. However, since the original problem is not convex, we perform successive convex approximation (SCA) and jointly optimize the parameters for the relaxed convex problem. Through extensive simulation, we validate the effectiveness of our proposed PHFL algorithm in terms of test accuracy, wall clock time, energy consumption and bandwidth requirement.

The COVID-19 pandemic presented numerous challenges to healthcare systems worldwide. Given that lung infections are prevalent among COVID-19 patients, chest Computer Tomography (CT) scans have frequently been utilized as an alternative method for identifying COVID-19 conditions and various other types of pulmonary diseases. Deep learning architectures have emerged to automate the identification of pulmonary disease types by leveraging CT scan slices as inputs for classification models. This paper introduces COVID-VR, a novel approach for classifying pulmonary diseases based on volume rendering images of the lungs captured from multiple angles, thereby providing a comprehensive view of the entire lung in each image. To assess the effectiveness of our proposal, we compared it against competing strategies utilizing both private data obtained from partner hospitals and a publicly available dataset. The results demonstrate that our approach effectively identifies pulmonary lesions and performs competitively when compared to slice-based methods.

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. Despite their versatile abilities, the larger question of their ability to reason remains ill-understood. This paper addresses reasoning in math word problems (MWPs) by studying symbolic versions of the numeric problems, since a symbolic expression is a "concise explanation" of the numeric answer. We create and use a symbolic version of the SVAMP dataset and find that GPT-3's davinci-002 model also has good zero-shot accuracy on symbolic MWPs. To evaluate the faithfulness of the model's reasoning, we go beyond accuracy and additionally evaluate the alignment between the final answer and the outputted reasoning, which correspond to numeric and symbolic answers respectively for MWPs. We explore a self-prompting approach to encourage the symbolic reasoning to align with the numeric answer, thus equipping the LLM with the ability to provide a concise and verifiable reasoning and making it more interpretable. Surprisingly, self-prompting also improves the symbolic accuracy to be higher than both the numeric and symbolic accuracies, thus providing an ensembling effect. The SVAMP_Sym dataset will be released for future research on symbolic math problems.

Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Nevertheless, recent studies have revealed that adversarially trained models are prone to unfairness problems, restricting their applicability. In this paper, we empirically observe that this limitation may be attributed to serious adversarial confidence overfitting, i.e., certain adversarial examples with overconfidence. To alleviate this problem, we propose HAM, a straightforward yet effective framework via adaptive Hard Adversarial example Mining.HAM concentrates on mining hard adversarial examples while discarding the easy ones in an adaptive fashion. Specifically, HAM identifies hard AEs in terms of their step sizes needed to cross the decision boundary when calculating loss value. Besides, an early-dropping mechanism is incorporated to discard the easy examples at the initial stages of AE generation, resulting in efficient AT. Extensive experimental results on CIFAR-10, SVHN, and Imagenette demonstrate that HAM achieves significant improvement in robust fairness while reducing computational cost compared to several state-of-the-art adversarial training methods. The code will be made publicly available.

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.

This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches, respectively, to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. It may be assumed that each reviewer has her own mapping from the set of features to a recommendation, and that different reviewers have different mappings in mind. This introduces an element of arbitrariness known as commensuration bias. In this paper we discuss a framework, introduced by Noothigattu, Shah and Procaccia, and then applied by the organizers of the AAAI 2022 conference. Noothigattu, Shah and Procaccia proposed to aggregate reviewer's mapping by minimizing certain loss functions, and studied axiomatic properties of this approach, in the sense of social choice theory. We challenge several of the results and assumptions used in their work and report a number of negative results. On the one hand, we study a trade-off between some of the axioms proposed and the ability of the method to properly capture agreements of the majority of reviewers. On the other hand, we show that dropping a certain unrealistic assumption has dramatic effects, including causing the method to be discontinuous.

At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks. Thus, this paper presents a carefully designed data creation pipeline for this purpose. Particularly, we initiate a dialogue between an AI investor and financial expert using ChatGPT and incorporate the feedback of human financial experts, leading to the refinement of the dataset. This pipeline yielded a robust instruction tuning dataset comprised of 103k multi-turn chats. Extensive experiments have been conducted on this dataset to evaluate the model's performance by adopting an external GPT-4 as the judge. The promising experimental results verify that our approach led to significant advancements in generating accurate, relevant, and financial-style responses from AI models, and thus providing a powerful tool for applications within the financial sector.

The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise. Specifically, we conduct theoretical analyses to reveal that label noise leads to weakened DNN generalization by loosening the PAC-Bayes generalization bound, and feature noise results in better DNN generalization by imposing an upper bound on the mutual information between the model weights and the features, which constrains the PAC-Bayes generalization bound. Furthermore, to ensure effective generalization of DNNs in the presence of label noise, we conduct application analyses to identify the optimal types and levels of feature noise to add for obtaining desirable label noise generalization. Finally, extensive experimental results on several popular datasets demonstrate the feature noise method can significantly enhance the label noise generalization of the state-of-the-art label noise method.

Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.

A common way to explore text corpora is through low-dimensional projections of the documents, where one hopes that thematically similar documents will be clustered together in the projected space. However, popular algorithms for dimensionality reduction of text corpora, like Latent Dirichlet Allocation (LDA), often produce projections that do not capture human notions of document similarity. We propose a semi-supervised human-in-the-loop LDA-based method for learning topics that preserve semantically meaningful relationships between documents in low-dimensional projections. On synthetic corpora, our method yields more interpretable projections than baseline methods with only a fraction of labels provided. On a real corpus, we obtain qualitatively similar results.

Depth completion, which aims to generate high-quality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation refinement methods require several iterations and suffer from a fixed receptive field, which may contain irrelevant and useless information with very sparse input. In this paper, we address these two challenges simultaneously by revisiting the idea of deformable convolution. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module, and empirically demonstrate its superiority. To better understand the function of deformable convolution and exploit it for depth completion, we further systematically investigate a variety of representative strategies. Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance. We evaluate our model on the large-scale KITTI dataset and achieve state-of-the-art level performance in both accuracy and inference speed. Our code is available at https://github.com/AlexSunNik/ReDC.

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2.48\% on the CIFAR-N dataset. The code is available at https://github.com/Justherozen/ProMix

Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention. In this paper, we provide quantitative metrics to assess the quality of link prediction explanations, with or without ground-truth. State-of-the-art explainability methods for Graph Neural Networks are evaluated using these metrics. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.

Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine five recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety.

An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.

This paper presents a fully automated approach for identifying speech anomalies from voice recordings to aid in the assessment of speech impairments. By combining Connectionist Temporal Classification (CTC) and encoder-decoder-based automatic speech recognition models, we generate rich acoustic and clean transcripts. We then apply several natural language processing methods to extract features from these transcripts to produce prototypes of healthy speech. Basic distance measures from these prototypes serve as input features for standard machine learning classifiers, yielding human-level accuracy for the distinction between recordings of people with aphasia and a healthy control group. Furthermore, the most frequently occurring aphasia types can be distinguished with 90% accuracy. The pipeline is directly applicable to other diseases and languages, showing promise for robustly extracting diagnostic speech biomarkers.

Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Networks (HGNNs), and their well-designed variants, on some commonly used benchmark graphs and hypergraphs, they are outperformed by even a simple Multi-Layer Perceptron. This observation motivates a reexamination of the design paradigm of the current GNNs and HGNNs and poses challenges of extracting graph features effectively. In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features via a normalized projection matrix. The resulting edge/hyperedge features, together with the original node features, are fed into a neural network. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix, of the network's output, which can be further used for tasks such as node classification. The proposed architecture, in contrast to the traditional spectral-based and/or message passing approaches, simultaneously and comprehensively exploits the node features and graph/hypergraph topologies in an efficient and unified manner, covering both heterophilic and homophilic graphs. The designed projection matrix, encoding the graph features, is intuitive and interpretable. Extensive experiments are conducted and demonstrate the superior performance of the proposed framework on twelve representative hypergraph datasets and six real-world graph datasets, compared to the state-of-the-art methods. Our implementation is available online at https://github.com/MinhZou/UniG-Encoder.

Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a robust decentralized learning framework for various client models where centralized data collection is refrained due to confidentiality concerns. This work aims to tackle privacy-preserving VQA by decoupling a multi-modal model into representation modules and a contrastive module and leveraging inter-module gradients sharing and inter-client weight sharing. To this end, we propose Bidirectional Contrastive Split Learning (BiCSL) to train a global multi-modal model on the entire data distribution of decentralized clients. We employ the contrastive loss that enables a more efficient self-supervised learning of decentralized modules. Comprehensive experiments are conducted on the VQA-v2 dataset based on five SOTA VQA models, demonstrating the effectiveness of the proposed method. Furthermore, we inspect BiCSL's robustness against a dual-key backdoor attack on VQA. Consequently, BiCSL shows much better robustness to the multi-modal adversarial attack compared to the centralized learning method, which provides a promising approach to decentralized multi-modal learning.

We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels. Deep neural networks are used to upscale artificially downscaled patches of levels from the puzzle platformer game Lode Runner. The trained networks are incorporated into a web-based editor, where the user can create and edit levels at three different levels of resolution: 4x4, 8x8, and 16x16. An edit at any resolution instantly transfers to the other resolutions. As upscaling requires inventing features that might not be present at lower resolutions, we train neural networks to reproduce these features. We introduce a neural network architecture that is capable of not only learning upscaling but also giving higher priority to less frequent tiles. To investigate the potential of this tool and guide further development, we conduct a qualitative study with 3 designers to understand how they use it. Designers enjoyed co-designing with the tool, liked its underlying concept, and provided feedback for further improvement.

An attractive book cover is important for the success of a book. In this paper, we apply Generative Adversarial Networks (GANs) to the book covers domain, using different methods for training in order to obtain better generated images. We interleave GANs with knowledge graphs to alter the input title to obtain multiple possible options for any given title, which are then used as an augmented input to the generator. Finally, we use the discriminator obtained during the training phase to select the best images generated with new titles. Our method performed better at generating book covers than previous attempts, and the knowledge graph gives better options to the book author or editor compared to using GANs alone.

Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: this http URL

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.

Distribution shifts are a serious concern in modern statistical learning as they can systematically change the properties of the data away from the truth. We focus on Wasserstein distribution shifts, where every data point may undergo a slight perturbation, as opposed to the Huber contamination model where a fraction of observations are outliers. We formulate and study shifts beyond independent perturbations, exploring Joint Distribution Shifts, where the per-observation perturbations can be coordinated. We analyze several important statistical problems, including location estimation, linear regression, and non-parametric density estimation. Under a squared loss for mean estimation and prediction error in linear regression, we find the exact minimax risk, a least favorable perturbation, and show that the sample mean and least squares estimators are respectively optimal. This holds for both independent and joint shifts, but the least favorable perturbations and minimax risks differ. For other problems, we provide nearly optimal estimators and precise finite-sample bounds. We also introduce several tools for bounding the minimax risk under distribution shift, such as a smoothing technique for location families, and generalizations of classical tools including least favorable sequences of priors, the modulus of continuity, Le Cam's, Fano's, and Assouad's methods.

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness against backdoor attacks still remains largely unexplored. In this paper, we focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We first provide a unified framework via randomized smoothing techniques and show how it can be instantiated to certify the robustness against both evasion and backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We prove the robustness bound for machine learning models trained with RAB and prove that our robustness bound is tight. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, support vector machines, and K-NN models on MNIST, CIFAR-10, and ImageNette datasets and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretic analysis and the comprehensive evaluation on diverse ML models and datasets shed light on further robust learning strategies against general training time attacks.

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.

Going beyond stochastic gradient descent (SGD), what new phenomena emerge in wide neural networks trained by adaptive optimizers like Adam? Here we show: The same dichotomy between feature learning and kernel behaviors (as in SGD) holds for general optimizers as well, including Adam -- albeit with a nonlinear notion of "kernel." We derive the corresponding "neural tangent" and "maximal update" limits for any architecture. Two foundational advances underlie the above results: 1) A new Tensor Program language, NEXORT, that can express how adaptive optimizers process gradients into updates. 2) The introduction of bra-ket notation to drastically simplify expressions and calculations in Tensor Programs. This work summarizes and generalizes all previous results in the Tensor Programs series of papers.

A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic control is a cognitively inspired approach relying on episodic memory, an instance-based memory model of an agent's experiences. Meanwhile, successor features and generalized policy improvement (SF&GPI) is a meta and transfer learning framework allowing to learn policies for tasks that can be efficiently reused for later tasks which have a different reward function. Individually, these two techniques have shown impressive results in vastly improving sample efficiency and the elegant reuse of previously learned policies. Thus, we outline a combination of both approaches in a single reinforcement learning framework and empirically illustrate its benefits.

Knowing the actual precipitation in space and time is critical in hydrological modelling applications, yet the spatial coverage with rain gauge stations is limited due to economic constraints. Gridded satellite precipitation datasets offer an alternative option for estimating the actual precipitation by covering uniformly large areas, albeit related estimates are not accurate. To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products. In this context, observed precipitation plays the role of the dependent variable, while satellite data play the role of predictor variables. Random forests is the dominant machine learning algorithm in relevant applications. In those spatial predictions settings, point predictions (mostly the mean or the median of the conditional distribution) of the dependent variable are issued. The aim of the manuscript is to solve the problem of probabilistic prediction of precipitation with an emphasis on extreme quantiles in spatial interpolation settings. Here we propose, issuing probabilistic spatial predictions of precipitation using Light Gradient Boosting Machine (LightGBM). LightGBM is a boosting algorithm, highlighted by prize-winning entries in prediction and forecasting competitions. To assess LightGBM, we contribute a large-scale application that includes merging daily precipitation measurements in contiguous US with PERSIANN and GPM-IMERG satellite precipitation data. We focus on extreme quantiles of the probability distribution of the dependent variable, where LightGBM outperforms quantile regression forests (QRF, a variant of random forests) in terms of quantile score at extreme quantiles. Our study offers understanding of probabilistic predictions in spatial settings using machine learning.

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake of the success of Topological Deep Learning (TDL), we study Latent Topology Inference (LTI) for learning higher-order cell complexes (with sparse and not regular topology) describing multi-way interactions between data points. To this aim, we introduce the Differentiable Cell Complex Module (DCM), a novel learnable function that computes cell probabilities in the complex to improve the downstream task. We show how to integrate DCM with cell complex message passing networks layers and train it in a end-to-end fashion, thanks to a two-step inference procedure that avoids an exhaustive search across all possible cells in the input, thus maintaining scalability. Our model is tested on several homophilic and heterophilic graph datasets and it is shown to outperform other state-of-the-art techniques, offering significant improvements especially in cases where an input graph is not provided.

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

This research paper delves into the integration of OpenAI's ChatGPT into embodied agent systems, evaluating its influence on interactive decision-making benchmark. Drawing a parallel to the concept of people assuming roles according to their unique strengths, we introduce InterAct. In this approach, we feed ChatGPT with varied prompts, assigning it a numerous roles like a checker and a sorter, then integrating them with the original language model. Our research shows a remarkable success rate of 98% in AlfWorld, which consists of 6 different tasks in a simulated household environment, emphasizing the significance of proficient prompt engineering. The results highlight ChatGPT's competence in comprehending and performing intricate tasks effectively in real-world settings, thus paving the way for further advancements in task planning.

The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so. To assess this, n = 145 depression and n =115 PTSD assessments and n = 46 clinical case studies across high prevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma and stress, Addictive disorders) were analyzed using prompts to extract estimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is capable of assessing psychiatric functioning across a range of psychiatric conditions with the strongest performance being the prediction of depression scores based on standardized assessments (Accuracy range= 0.80 - 0.84) which were statistically indistinguishable from human clinical raters t(1,144) = 1.20; p = 0.23. Results show the potential for general clinical language models to flexibly predict psychiatric risk based on free descriptions of functioning from both patients and clinicians.

Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution of parametrized time-dependent nonlinear partial differential equations (PDEs). In this context, full order models (FOMs), such as those relying on the finite element method, can reach high levels of accuracy, however often yielding intensive simulations to run. For this reason, surrogate models are developed to replace computationally expensive solvers with more efficient ones, which can strike favorable trade-offs between accuracy and efficiency. This work explores the potential usage of graph neural networks (GNNs) for the simulation of time-dependent PDEs in the presence of geometrical variability. In particular, we propose a systematic strategy to build surrogate models based on a data-driven time-stepping scheme where a GNN architecture is used to efficiently evolve the system. With respect to the majority of surrogate models, the proposed approach stands out for its ability of tackling problems with parameter dependent spatial domains, while simultaneously generalizing to different geometries and mesh resolutions. We assess the effectiveness of the proposed approach through a series of numerical experiments, involving both two- and three-dimensional problems, showing that GNNs can provide a valid alternative to traditional surrogate models in terms of computational efficiency and generalization to new scenarios. We also assess, from a numerical standpoint, the importance of using GNNs, rather than classical dense deep neural networks, for the proposed framework.

Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank; low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.

We study the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. We show that the convergence rates of the covariance estimator are $O\big(\sqrt{d}\,n^{-1/8}(\log n)^{1/4}\big)$ and $O\big(\sqrt{d}\,n^{-1/8}\big)$ under state-dependent and state-independent Markovian sampling, respectively, with $d$ representing dimensionality and $n$ denoting the number of observations or SGD iterations. Remarkably, these rates match the best-known convergence rate previously established for the independent and identically distributed ($\iid$) case by \cite{zhu2021online}, up to logarithmic factors. Our analysis overcomes significant challenges that arise due to Markovian sampling, leading to the introduction of additional error terms and complex dependencies between the blocks of the batch-means covariance estimator. Moreover, we establish the convergence rate for the first four moments of the $\ell_2$ norm of the error of SGD dynamics under state-dependent Markovian data, which holds potential interest as an independent result. To validate our theoretical findings, we provide numerical illustrations to derive confidence intervals for SGD when training linear and logistic regression models under Markovian sampling. Additionally, we apply our approach to tackle the intriguing problem of strategic classification with logistic regression, where adversaries can adaptively modify features during the training process to increase their chances of being classified in a specific target class.

We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor_tSVD}. We build on a recent line of results that considered convex relaxations for the recovery of low-rank matrices and established that under a strict complementarity condition (SC), both the convergence rate and per-iteration runtime of standard gradient methods may improve dramatically. We develop the appropriate strict complementarity condition for the tensor nuclear norm ball and obtain the following main results under this condition: 1. When the objective to minimize is of the form $f(\mX)=g(\mA\mX)+\langle{\mC,\mX}\rangle$ , where $g$ is strongly convex and $\mA$ is a linear map (e.g., least squares), a quadratic growth bound holds, which implies linear convergence rates for standard projected gradient methods, despite the fact that $f$ need not be strongly convex. 2. For a smooth objective function, when initialized in certain proximity of an optimal solution which satisfies SC, standard projected gradient methods only require SVD computations (for projecting onto the tensor nuclear norm ball) of rank that matches the tubal rank of the optimal solution. In particular, when the tubal rank is constant, this implies nearly linear (in the size of the tensor) runtime per iteration, as opposed to super linear without further assumptions. 3. For a nonsmooth objective function which admits a popular smooth saddle-point formulation, we derive similar results to the latter for the well known extragradient method. An additional contribution which may be of independent interest, is the rigorous extension of many basic results regarding tensors of arbitrary order, which were previously obtained only for third-order tensors.

In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on the case of least-squares regression (LSR) and analyze a general stochastic approximation algorithm for minimizing quadratic functions relying on a random field. We consider weak assumptions on the random field, tailored to the analysis (specifically, expected H\"older regularity), and on the noise covariance, enabling the analysis of various randomizing mechanisms, including compression. We then extend our results to the case of federated learning. More formally, we highlight the impact on the convergence of the covariance $\mathfrak{C}_{\mathrm{ania}}$ of the additive noise induced by the algorithm. We demonstrate despite the non-regularity of the stochastic field, that the limit variance term scales with $\mathrm{Tr}(\mathfrak{C}_{\mathrm{ania}} H^{-1})/K$ (where $H$ is the Hessian of the optimization problem and $K$ the number of iterations) generalizing the rate for the vanilla LSR case where it is $\sigma^2 \mathrm{Tr}(H H^{-1}) / K = \sigma^2 d / K$ (Bach and Moulines, 2013). Then, we analyze the dependency of $\mathfrak{C}_{\mathrm{ania}}$ on the compression strategy and ultimately its impact on convergence, first in the centralized case, then in two heterogeneous FL frameworks.

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings.

Despite the proliferation of diverse hardware accelerators (e.g., NPU, TPU, DPU), deploying deep learning models on edge devices with fixed-point hardware is still challenging due to complex model quantization and conversion. Existing model quantization frameworks like Tensorflow QAT [1], TFLite PTQ [2], and Qualcomm AIMET [3] supports only a limited set of quantization schemes (e.g., only asymmetric per-tensor quantization in TF1.x QAT [4]). Accordingly, deep learning models cannot be easily quantized for diverse fixed-point hardwares, mainly due to slightly different quantization requirements. In this paper, we envision a new type of model quantization approach called MRQ (model re-quantization), which takes existing quantized models and quickly transforms the models to meet different quantization requirements (e.g., asymmetric -> symmetric, non-power-of-2 scale -> power-of-2 scale). Re-quantization is much simpler than quantizing from scratch because it avoids costly re-training and provides support for multiple quantization schemes simultaneously. To minimize re-quantization error, we developed a new set of re-quantization algorithms including weight correction and rounding error folding. We have demonstrated that MobileNetV2 QAT model [7] can be quickly re-quantized into two different quantization schemes (i.e., symmetric and symmetric+power-of-2 scale) with less than 0.64 units of accuracy loss. We believe our work is the first to leverage this concept of re-quantization for model quantization and models obtained from the re-quantization process have been successfully deployed on NNA in the Echo Show devices.

Identification of nonlinear dynamical systems has been popularized by sparse identification of the nonlinear dynamics (SINDy) via the sequentially thresholded least squares (STLS) algorithm. Many extensions SINDy have emerged in the literature to deal with experimental data which are finite in length and noisy. Recently, the computationally intensive method of ensembling bootstrapped SINDy models (E-SINDy) was proposed for model identification, handling finite, highly noisy data. While the extensions of SINDy are numerous, their sparsity-promoting estimators occasionally provide sparse approximations of the dynamics as opposed to exact recovery. Furthermore, these estimators suffer under multicollinearity, e.g. the irrepresentable condition for the Lasso. In this paper, we demonstrate that the Trimmed Lasso for robust identification of models (TRIM) can provide exact recovery under more severe noise, finite data, and multicollinearity as opposed to E-SINDy. Additionally, the computational cost of TRIM is asymptotically equal to STLS since the sparsity parameter of the TRIM can be solved efficiently by convex solvers. We compare these methodologies on challenging nonlinear systems, specifically the Lorenz 63 system, the Bouc Wen oscillator from the nonlinear dynamics benchmark of No\"el and Schoukens, 2016, and a time delay system describing tool cutting dynamics. This study emphasizes the comparisons between STLS, reweighted $\ell_1$ minimization, and Trimmed Lasso in identification with respect to problems faced by practitioners: the problem of finite and noisy data, the performance of the sparse regression of when the library grows in dimension (multicollinearity), and automatic methods for choice of regularization parameters.

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.

Are current language models capable of deception and lie detection? We study this question by introducing a text-based game called $\textit{Hoodwinked}$, inspired by $\textit{Mafia}$ and $\textit{Among Us}$. Players are locked in a house and must find a key to escape, but one player is tasked with killing the others. Each time a murder is committed, the surviving players have a natural language discussion then vote to banish one player from the game. We conduct experiments with agents controlled by GPT-3, GPT-3.5, and GPT-4 and find evidence of deception and lie detection capabilities. The killer often denies their crime and accuses others, leading to measurable effects on voting outcomes. More advanced models are more effective killers, outperforming smaller models in 18 of 24 pairwise comparisons. Secondary metrics provide evidence that this improvement is not mediated by different actions, but rather by stronger deception capabilities during discussions. Overall, we find substantial evidence that current language models are capable of deception. To better evaluate the ability of AI agents to deceive humans, we make this game publicly available at https://hoodwinked.ai/ .

Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data.

The diffusion model is capable of generating high-quality data through a probabilistic approach. However, it suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. To address this limitation, recent models such as denoising diffusion implicit models (DDIM) focus on generating samples without directly modeling the probability distribution, while models like denoising diffusion generative adversarial networks (GAN) combine diffusion processes with GANs. In the field of speech synthesis, a recent diffusion speech synthesis model called DiffGAN-TTS, utilizing the structure of GANs, has been introduced and demonstrates superior performance in both speech quality and generation speed. In this paper, to further enhance the performance of DiffGAN-TTS, we propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data. Objective metrics such as structural similarity index measure (SSIM), mel-cepstral distortion (MCD), F0 root mean squared error (F0 RMSE), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), as well as subjective metrics like mean opinion score (MOS), are used to evaluate the performance of the proposed model. The evaluation results show that the proposed model outperforms recent state-of-the-art models such as FastSpeech2 and DiffGAN-TTS in various metrics. Our implementation and audio samples are located on GitHub.

In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have driven the development of efficient, approximate unlearning techniques. Moving beyond data-centric MU approaches, our study introduces a novel model-based perspective: model sparsification via weight pruning, which is capable of reducing the gap between exact unlearning and approximate unlearning. We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. This leads to a new MU paradigm, termed prune first, then unlearn, which infuses a sparse model prior into the unlearning process. Building on this insight, we also develop a sparsity-aware unlearning method that utilizes sparsity regularization to enhance the training process of approximate unlearning. Extensive experiments show that our proposals consistently benefit MU in various unlearning scenarios. A notable highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest unlearning methods) when using sparsity-aware unlearning. Furthermore, we demonstrate the practical impact of our proposed MU methods in addressing other machine learning challenges, such as defending against backdoor attacks and enhancing transfer learning. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse.

Job scheduling is a well-known Combinatorial Optimization problem with endless applications. Well planned schedules bring many benefits in the context of automated systems: among others, they limit production costs and waste. Nevertheless, the NP-hardness of this problem makes it essential to use heuristics whose design is difficult, requires specialized knowledge and often produces methods tailored to the specific task. This paper presents an original end-to-end Deep Reinforcement Learning approach to scheduling that automatically learns dispatching rules. Our technique is inspired by natural language encoder-decoder models for sequence processing and has never been used, to the best of our knowledge, for scheduling purposes. We applied and tested our method in particular to some benchmark instances of Job Shop Problem, but this technique is general enough to be potentially used to tackle other different optimal job scheduling tasks with minimal intervention. Results demonstrate that we outperform many classical approaches exploiting priority dispatching rules and show competitive results on state-of-the-art Deep Reinforcement Learning ones.

Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we propose learning diffusion models as priors. We then can sample directly from the posterior trajectory distribution conditioned on task goals, by leveraging the inverse denoising process of diffusion models. Furthermore, diffusion has been recently shown to effectively encode data multimodality in high-dimensional settings, which is particularly well-suited for large trajectory dataset. To demonstrate our method efficacy, we compare our proposed method - Motion Planning Diffusion - against several baselines in simulated planar robot and 7-dof robot arm manipulator environments. To assess the generalization capabilities of our method, we test it in environments with previously unseen obstacles. Our experiments show that diffusion models are strong priors to encode high-dimensional trajectory distributions of robot motions.

One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes the corresponding misclassification probabilities. The paper presents a resampling framework to construct exact, distribution-free and non-asymptotically guaranteed confidence regions for the true regression function for any user-chosen confidence level. Then, specific algorithms are suggested to demonstrate the framework. It is proved that the constructed confidence regions are strongly consistent, that is, any false model is excluded in the long run with probability one. The exclusion is quantified with probably approximately correct type bounds, as well. Finally, the algorithms are validated via numerical experiments, and the methods are compared to approximate asymptotic confidence ellipsoids.

$Q$ learning is a popular model free reinforcement learning method. Most of existing works focus on analyzing $Q$ learning for finite state and action spaces. If the state space is continuous, then the original $Q$ learning method can not be directly used. A modification of the original $Q$ learning method was proposed in (Shah and Xie, 2018), which estimates $Q$ values with nearest neighbors. Such modification makes $Q$ learning suitable for continuous state space. (Shah and Xie, 2018) shows that the convergence rate of estimated $Q$ function is $\tilde{O}(T^{-1/(d+3)})$, which is slower than the minimax lower bound $\tilde{\Omega}(T^{-1/(d+2)})$, indicating that this method is not efficient. This paper proposes two new $Q$ learning methods to bridge the gap of convergence rates in (Shah and Xie, 2018), with one of them being offline, while the other is online. Despite that we still use nearest neighbor approach to estimate $Q$ function, the algorithms are crucially different from (Shah and Xie, 2018). In particular, we replace the kernel nearest neighbor in discretized region with a direct nearest neighbor approach. Consequently, our approach significantly improves the convergence rate. Moreover, the time complexity is also significantly improved in high dimensional state spaces. Our analysis shows that both offline and online methods are minimax rate optimal.

Face swapping is a task that changes a facial identity of a given image to that of another person. In this work, we propose a novel face-swapping framework called Megapixel Facial Identity Manipulation (MFIM). The face-swapping model should achieve two goals. First, it should be able to generate a high-quality image. We argue that a model which is proficient in generating a megapixel image can achieve this goal. However, generating a megapixel image is generally difficult without careful model design. Therefore, our model exploits pretrained StyleGAN in the manner of GAN-inversion to effectively generate a megapixel image. Second, it should be able to effectively transform the identity of a given image. Specifically, it should be able to actively transform ID attributes (e.g., face shape and eyes) of a given image into those of another person, while preserving ID-irrelevant attributes (e.g., pose and expression). To achieve this goal, we exploit 3DMM that can capture various facial attributes. Specifically, we explicitly supervise our model to generate a face-swapped image with the desirable attributes using 3DMM. We show that our model achieves state-of-the-art performance through extensive experiments. Furthermore, we propose a new operation called ID mixing, which creates a new identity by semantically mixing the identities of several people. It allows the user to customize the new identity.

In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matrices turn out to be a class of matrices which correspond to Hebbian kernels revised by iteratively applying some unlearning protocols. Remarkably, the number of unlearning steps is proved to be related to the regularization hyperparameters of the loss function and to the training time. Thus, we can design strategies to avoid overfitting that are formulated in terms of the algebraic properties of the interaction matrix, or, equivalently, in terms of regularization tuning and early-stopping strategies. The generalization capabilities of these attractor networks are also investigated: analytical results are obtained for random synthetic datasets, next, the emerging picture is corroborated by numerical experiments that highlight the existence of several regimes (i.e., overfitting, failure and success) as the dataset parameters are varied.

Human intelligence excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive intelligent models, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research platform designed for multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models, tools or APIs for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI. We are open-sourcing the OpenAGI project's code, dataset, benchmarks, evaluation methods, and demo to foster community involvement in AGI advancement: https://github.com/agiresearch/OpenAGI.  ( 3 min )

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/  ( 3 min )

Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: Action Units, Valence Arousal (VA), and Categorical Emotions. As part of an effort to move beyond these representations towards more fine-grained labels, we describe our submission to the newly introduced Emotional Reaction Intensity (ERI) Estimation challenge in the 5th competition for Affective Behavior Analysis in-the-Wild (ABAW). We developed four deep neural networks trained in the visual domain and a multimodal model trained with both visual and audio features to predict emotion reaction intensity. Our best performing model on the Hume-Reaction dataset achieved an average Pearson correlation coefficient of 0.4080 on the test set using a pre-trained ResNet50 model. This work provides a first step towards the development of production-grade models which predict emotion reaction intensities rather than discrete emotion categories.  ( 2 min )

Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions. It is architecture agnostic and can be applied to any feed-forward deterministic network without changes to the architecture or training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method's ability to generate diverse and multi-modal predictive distributions, and a desirable correlation of the estimated uncertainty with the prediction error.  ( 2 min )

Fine-tuning language models in a downstream task is the standard approach for many state-of-the-art methodologies in the field of NLP. However, when the distribution between the source task and target task drifts, \textit{e.g.}, conversational environments, these gains tend to be diminished. This article proposes a sequence of pre-training steps (a curriculum) guided by "data hacking" and grammar analysis that allows further gradual adaptation between pre-training distributions. In our experiments, we acquire a considerable improvement from our method compared to other known pre-training approaches for the MultiWoZ task.  ( 2 min )

Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, BLO is a classical optimization problem that involves two levels of hierarchy (i.e., upper and lower levels), wherein obtaining the solution to the upper-level problem requires solving the lower-level one. BLO has become popular largely because it is powerful in modeling problems in SP and ML, among others, that involve optimizing nested objective functions. Prominent applications of BLO range from resource allocation for wireless systems to adversarial machine learning. In this work, we focus on a class of tractable BLO problems that often appear in SP and ML applications. We provide an overview of some basic concepts of this class of BLO problems, such as their optimality conditions, standard algorithms (including their optimization principles and practical implementations), as well as how they can be leveraged to obtain state-of-the-art results for a number of key SP and ML applications. Further, we discuss some recent advances in BLO theory, its implications for applications, and point out some limitations of the state-of-the-art that require significant future research efforts. Overall, we hope that this article can serve to accelerate the adoption of BLO as a generic tool to model, analyze, and innovate on a wide array of emerging SP and ML applications.  ( 3 min )

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets.  ( 2 min )

Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals -- which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.  ( 2 min )

Real-time rendering for video games has become increasingly challenging due to the need for higher resolutions, framerates and photorealism. Supersampling has emerged as an effective solution to address this challenge. Our work introduces a novel neural algorithm for supersampling rendered content that is 4 times more efficient than existing methods while maintaining the same level of accuracy. Additionally, we introduce a new dataset which provides auxiliary modalities such as motion vectors and depth generated using graphics rendering features like viewport jittering and mipmap biasing at different resolutions. We believe that this dataset fills a gap in the current dataset landscape and can serve as a valuable resource to help measure progress in the field and advance the state-of-the-art in super-resolution techniques for gaming content.  ( 2 min )

We present a new algorithm for automatically bounding the Taylor remainder series. In the special case of a scalar function $f: \mathbb{R} \to \mathbb{R}$, our algorithm takes as input a reference point $x_0$, trust region $[a, b]$, and integer $k \ge 1$, and returns an interval $I$ such that $f(x) - \sum_{i=0}^{k-1} \frac {1} {i!} f^{(i)}(x_0) (x - x_0)^i \in I (x - x_0)^k$ for all $x \in [a, b]$. As in automatic differentiation, the function $f$ is provided to the algorithm in symbolic form, and must be composed of known atomic functions. At a high level, our algorithm has two steps. First, for a variety of commonly-used elementary functions (e.g., $\exp$, $\log$), we use recently-developed theory to derive sharp polynomial upper and lower bounds on the Taylor remainder series. We then recursively combine the bounds for the elementary functions using an interval arithmetic variant of Taylor-mode automatic differentiation. Our algorithm can make efficient use of machine learning hardware accelerators, and we provide an open source implementation in JAX. We then turn our attention to applications. Most notably, in a companion paper we use our new machinery to create the first universal majorization-minimization optimization algorithms: algorithms that iteratively minimize an arbitrary loss using a majorizer that is derived automatically, rather than by hand. We also show that our automatically-derived bounds can be used for verified global optimization and numerical integration, and to prove sharper versions of Jensen's inequality.  ( 3 min )

With the advancement of DNNs into safety-critical applications, testing approaches for such models have gained more attention. A current direction is the search for and identification of systematic weaknesses that put safety assumptions based on average performance values at risk. Such weaknesses can take on the form of (semantically coherent) subsets or areas in the input space where a DNN performs systematically worse than its expected average. However, it is non-trivial to attribute the reason for such observed low performances to the specific semantic features that describe the subset. For instance, inhomogeneities within the data w.r.t. other (non-considered) attributes might distort results. However, taking into account all (available) attributes and their interaction is often computationally highly expensive. Inspired by counterfactual explanations, we propose an effective and computationally cheap algorithm to validate the semantic attribution of existing subsets, i.e., to check whether the identified attribute is likely to have caused the degraded performance. We demonstrate this approach on an example from the autonomous driving domain using highly annotated simulated data, where we show for a semantic segmentation model that (i) performance differences among the different pedestrian assets exist, but (ii) only in some cases is the asset type itself the reason for this reduction in the performance.  ( 2 min )

ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate of metal-organic frameworks (MOFs). By leveraging a large-scale language model (gpt-3.5-turbo), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid structured queries. The system is comprised of three core components (i.e. an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generation. The study further explores the merits and constraints of using large language models (LLMs) AI system in material sciences using and showcases its transformative potential for future advancements.  ( 2 min )

To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them. While current agents learn to execute simple language instructions from task rewards, we aim to build agents that leverage diverse language that conveys general knowledge, describes the state of the world, provides interactive feedback, and more. Our key idea is that language helps agents predict the future: what will be observed, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning objective. We present Dynalang, an agent that learns a multimodal world model that predicts future text and image representations and learns to act from imagined model rollouts. Unlike traditional agents that use language only to predict actions, Dynalang acquires rich language understanding by using past language also to predict future language, video, and rewards. In addition to learning from online interaction in an environment, Dynalang can be pretrained on datasets of text, video, or both without actions or rewards. From using language hints in grid worlds to navigating photorealistic scans of homes, Dynalang utilizes diverse types of language to improve task performance, including environment descriptions, game rules, and instructions.  ( 2 min )

Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual and visual spaces to compensate for limited image labels, yet may suffer from reduced accuracy due to the scarcity of high-quality multi-label annotations. In this research, we leverage the powerful alignment between textual and visual features pretrained with millions of auxiliary image-text pairs. We introduce an efficient and effective framework called Evidence-guided Dual Context Optimization (DualCoOp++), which serves as a unified approach for addressing partial-label and zero-shot multi-label recognition. In DualCoOp++ we separately encode evidential, positive, and negative contexts for target classes as parametric components of the linguistic input (i.e., prompts). The evidential context aims to discover all the related visual content for the target class, and serves as guidance to aggregate positive and negative contexts from the spatial domain of the image, enabling better distinguishment between similar categories. Additionally, we introduce a Winner-Take-All module that promotes inter-class interaction during training, while avoiding the need for extra parameters and costs. As DualCoOp++ imposes minimal additional learnable overhead on the pretrained vision-language framework, it enables rapid adaptation to multi-label recognition tasks with limited annotations and even unseen classes. Experiments on standard multi-label recognition benchmarks across two challenging low-label settings demonstrate the superior performance of our approach compared to state-of-the-art methods.  ( 3 min )

Nowadays, autonomous cars are gaining traction due to their numerous potential applications on battlefields and in resolving a variety of other real-world challenges. The main goal of our project is to build an autonomous system using DeepRacer which will follow a specific person (for our project, a soldier) when they will be moving in any direction. Two main components to accomplish this project is an optimized Single-Shot Multibox Detection (SSD) object detection model and a Reinforcement Learning (RL) model. We accomplished the task using SSD Lite instead of SSD and at the end, compared the results among SSD, SSD with Neural Computing Stick (NCS), and SSD Lite. Experimental results show that SSD Lite gives better performance among these three techniques and exhibits a considerable boost in inference speed (~2-3 times) without compromising accuracy.  ( 2 min )

Continual learning seeks to enable deep learners to train on a series of tasks of unknown length without suffering from the catastrophic forgetting of previous tasks. One effective solution is replay, which involves storing few previous experiences in memory and replaying them when learning the current task. However, there is still room for improvement when it comes to selecting the most informative samples for storage and determining the optimal number of samples to be stored. This study aims to address these issues with a novel comparison of the commonly used reservoir sampling to various alternative population strategies and providing a novel detailed analysis of how to find the optimal number of stored samples.  ( 2 min )

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.  ( 3 min )

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.  ( 2 min )

Machine learning models are known to be vulnerable to adversarial evasion attacks as illustrated by image classification models. Thoroughly understanding such attacks is critical in order to ensure the safety and robustness of critical AI tasks. However, most evasion attacks are difficult to deploy against a majority of AI systems because they have focused on image domain with only few constraints. An image is composed of homogeneous, numerical, continuous, and independent features, unlike many other input types to AI systems used in practice. Furthermore, some input types include additional semantic and functional constraints that must be observed to generate realistic adversarial inputs. In this work, we propose a new framework to enable the generation of adversarial inputs irrespective of the input type and task domain. Given an input and a set of pre-defined input transformations, our framework discovers a sequence of transformations that result in a semantically correct and functional adversarial input. We demonstrate the generality of our approach on several diverse machine learning tasks with various input representations. We also show the importance of generating adversarial examples as they enable the deployment of mitigation techniques.  ( 2 min )

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Secondly, a better loglikelihood does not guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favoring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods.  ( 3 min )

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the Combined Actuarial Neural Network (CANN) framework proposed by Mario W\"uthrich and Michael Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial (MVNB) specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data. SMAI provides a statistical test to robustly determine the alignability between datasets to avoid misleading inference, and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for instance Concrete dropout (Gal et al., 2017) and Deep Ensembles (Lakshminarayanan et al., 2017). Specifically, an MVE network assumes that the data is produced from a normal distribution with a mean function and variance function. The MVE network outputs a mean and variance estimate and optimizes the network parameters by minimizing the negative loglikelihood. In our paper, we present two significant insights. Firstly, the convergence difficulties reported in recent work can be relatively easily prevented by following the simple yet often overlooked recommendation from the original authors that a warm-up period should be used. During this period, only the mean is optimized with a fixed variance. We demonstrate the effectiveness of this step through experimentation, highlighting that it should be standard practice. As a sidenote, we examine whether, after the warm-up, it is beneficial to fix the mean while optimizing the variance or to optimize both simultaneously. Here, we do not observe a substantial difference. Secondly, we introduce a novel improvement of the MVE network: separate regularization of the mean and the variance estimate. We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.

An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate their compatibility with anatomical and physiological observations. Similarity matching objectives have served as successful starting points for deriving online algorithms that map onto neural networks (NNs) with point neurons and Hebbian/anti-Hebbian plasticity. These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain. In this article, we unify and generalize recent extensions of the similarity matching approach to address more complex objectives, including a large class of unsupervised and self-supervised learning tasks that can be formulated as symmetric generalized eigenvalue problems or nonnegative matrix factorization problems. Interestingly, the online algorithms derived from these objectives naturally map onto NNs with multi-compartmental neurons and local, non-Hebbian learning rules. Therefore, this unified extension of the similarity matching approach provides a normative framework that facilitates understanding multi-compartmental neuronal structures and non-Hebbian plasticity found throughout the brain.

We study the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling. We show that the convergence rates of the covariance estimator are $O\big(\sqrt{d}\,n^{-1/8}(\log n)^{1/4}\big)$ and $O\big(\sqrt{d}\,n^{-1/8}\big)$ under state-dependent and state-independent Markovian sampling, respectively, with $d$ representing dimensionality and $n$ denoting the number of observations or SGD iterations. Remarkably, these rates match the best-known convergence rate previously established for the independent and identically distributed ($\iid$) case by \cite{zhu2021online}, up to logarithmic factors. Our analysis overcomes significant challenges that arise due to Markovian sampling, leading to the introduction of additional error terms and complex dependencies between the blocks of the batch-means covariance estimator. Moreover, we establish the convergence rate for the first four moments of the $\ell_2$ norm of the error of SGD dynamics under state-dependent Markovian data, which holds potential interest as an independent result. To validate our theoretical findings, we provide numerical illustrations to derive confidence intervals for SGD when training linear and logistic regression models under Markovian sampling. Additionally, we apply our approach to tackle the intriguing problem of strategic classification with logistic regression, where adversaries can adaptively modify features during the training process to increase their chances of being classified in a specific target class.

$Q$ learning is a popular model free reinforcement learning method. Most of existing works focus on analyzing $Q$ learning for finite state and action spaces. If the state space is continuous, then the original $Q$ learning method can not be directly used. A modification of the original $Q$ learning method was proposed in (Shah and Xie, 2018), which estimates $Q$ values with nearest neighbors. Such modification makes $Q$ learning suitable for continuous state space. (Shah and Xie, 2018) shows that the convergence rate of estimated $Q$ function is $\tilde{O}(T^{-1/(d+3)})$, which is slower than the minimax lower bound $\tilde{\Omega}(T^{-1/(d+2)})$, indicating that this method is not efficient. This paper proposes two new $Q$ learning methods to bridge the gap of convergence rates in (Shah and Xie, 2018), with one of them being offline, while the other is online. Despite that we still use nearest neighbor approach to estimate $Q$ function, the algorithms are crucially different from (Shah and Xie, 2018). In particular, we replace the kernel nearest neighbor in discretized region with a direct nearest neighbor approach. Consequently, our approach significantly improves the convergence rate. Moreover, the time complexity is also significantly improved in high dimensional state spaces. Our analysis shows that both offline and online methods are minimax rate optimal.

We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to lower order interaction terms. The idea is to modify the random forest algorithm by keeping certain leaves after they are split instead of deleting them. This leads to non-binary trees which we refer to as planted trees. An extension to a forest leads to our random planted forest algorithm. Additionally, the maximum number of covariates which can interact within a leaf can be bounded. If we set this interaction bound to one, the resulting estimator is a sum of one-dimensional functions. In the other extreme case, if we do not set a limit, the resulting estimator and corresponding model place no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealized version of random planted forests in cases where the interaction bound is low. We show that if it is smaller than three, the idealized version achieves asymptotically optimal convergence rates up to a logarithmic factor. Code is available on GitHub https://github.com/PlantedML/randomPlantedForest.

Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks such as error correction, learning, and optimization. Despite the empirical success of utilizing these systems for intelligent tasks, the underlying principles that govern their emergent intelligent behaviors remain largely unknown. In this thesis, we aim to characterize such emergent intelligence in disordered systems through statistical physics. We chart a roadmap for our efforts in this thesis based on two axes: learning mechanisms (long-term memory vs. working memory) and learning dynamics (artificial vs. natural). Throughout our journey, we uncover relationships between learning mechanisms and physical dynamics that could serve as guiding principles for designing intelligent systems. We hope that our investigation into the emergent intelligence of seemingly disparate learning systems can expand our current understanding of intelligence beyond neural systems and uncover a wider range of computational substrates suitable for AI applications.

Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness against backdoor attacks still remains largely unexplored. In this paper, we focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We first provide a unified framework via randomized smoothing techniques and show how it can be instantiated to certify the robustness against both evasion and backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We prove the robustness bound for machine learning models trained with RAB and prove that our robustness bound is tight. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm that eliminates the need to sample from a noise distribution for such models. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, support vector machines, and K-NN models on MNIST, CIFAR-10, and ImageNette datasets and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretic analysis and the comprehensive evaluation on diverse ML models and datasets shed light on further robust learning strategies against general training time attacks.

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output a prediction interval directly. These methods are often tested by examining the fraction of test points that fall inside the corresponding prediction intervals. Intuitively both approaches seem logical. However, we demonstrate through both theoretical arguments and simulations that both ways of evaluating the quality of uncertainty estimates have serious flaws. Firstly, both approaches cannot disentangle the separate components that jointly create the predictive uncertainty, making it difficult to evaluate the quality of the estimates of these components. Secondly, a better loglikelihood does not guarantee better prediction intervals, which is what the methods are often used for in practice. Moreover, the current approach to test prediction intervals directly has additional flaws. We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set. At best, marginal coverage is measured, implicitly averaging out overconfident and underconfident predictions. A much more desirable property is pointwise coverage, requiring the correct coverage for each prediction. We demonstrate through practical examples that these effects can result in favoring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence or prediction intervals. Finally, we propose a simulation-based testing approach that addresses these problems while still allowing easy comparison between different methods.

Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven decisions. Here, we give an introduction to the key elements, focusing on routinely-collected data, electronic health records (EHRs) and claims data. Using such data to assess the value of an intervention requires care: temporal dependencies and existing practices easily confound the causal effect. We present a step-by-step framework to help build valid decision making from real-life patient records by emulating a randomized trial before individualizing decisions, eg with machine learning. Our framework highlights the most important pitfalls and considerations in analysing EHRs or claims data to draw causal conclusions. We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV). We study the impact of various choices at every step, from feature extraction to causal-estimator selection. In a tutorial spirit, the code and the data are openly available.

In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on the case of least-squares regression (LSR) and analyze a general stochastic approximation algorithm for minimizing quadratic functions relying on a random field. We consider weak assumptions on the random field, tailored to the analysis (specifically, expected H\"older regularity), and on the noise covariance, enabling the analysis of various randomizing mechanisms, including compression. We then extend our results to the case of federated learning. More formally, we highlight the impact on the convergence of the covariance $\mathfrak{C}_{\mathrm{ania}}$ of the additive noise induced by the algorithm. We demonstrate despite the non-regularity of the stochastic field, that the limit variance term scales with $\mathrm{Tr}(\mathfrak{C}_{\mathrm{ania}} H^{-1})/K$ (where $H$ is the Hessian of the optimization problem and $K$ the number of iterations) generalizing the rate for the vanilla LSR case where it is $\sigma^2 \mathrm{Tr}(H H^{-1}) / K = \sigma^2 d / K$ (Bach and Moulines, 2013). Then, we analyze the dependency of $\mathfrak{C}_{\mathrm{ania}}$ on the compression strategy and ultimately its impact on convergence, first in the centralized case, then in two heterogeneous FL frameworks.

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.

We consider the degree-Rips construction from topological data analysis, which provides a density-sensitive, multiparameter hierarchical clustering algorithm. We analyze its stability to perturbations of the input data using the correspondence-interleaving distance, a metric for hierarchical clusterings that we introduce. Taking certain one-parameter slices of degree-Rips recovers well-known methods for density-based clustering, but we show that these methods are unstable. However, we prove that degree-Rips, as a multiparameter object, is stable, and we propose an alternative approach for taking slices of degree-Rips, which yields a one-parameter hierarchical clustering algorithm with better stability properties. We prove that this algorithm is consistent, using the correspondence-interleaving distance. We provide an algorithm for extracting a single clustering from one-parameter hierarchical clusterings, which is stable with respect to the correspondence-interleaving distance. And, we integrate these methods into a pipeline for density-based clustering, which we call Persistable. Adapting tools from multiparameter persistent homology, we propose visualization tools that guide the selection of all parameters of the pipeline. We demonstrate Persistable on benchmark datasets, showing that it identifies multi-scale cluster structure in data.

We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor_tSVD}. We build on a recent line of results that considered convex relaxations for the recovery of low-rank matrices and established that under a strict complementarity condition (SC), both the convergence rate and per-iteration runtime of standard gradient methods may improve dramatically. We develop the appropriate strict complementarity condition for the tensor nuclear norm ball and obtain the following main results under this condition: 1. When the objective to minimize is of the form $f(\mX)=g(\mA\mX)+\langle{\mC,\mX}\rangle$ , where $g$ is strongly convex and $\mA$ is a linear map (e.g., least squares), a quadratic growth bound holds, which implies linear convergence rates for standard projected gradient methods, despite the fact that $f$ need not be strongly convex. 2. For a smooth objective function, when initialized in certain proximity of an optimal solution which satisfies SC, standard projected gradient methods only require SVD computations (for projecting onto the tensor nuclear norm ball) of rank that matches the tubal rank of the optimal solution. In particular, when the tubal rank is constant, this implies nearly linear (in the size of the tensor) runtime per iteration, as opposed to super linear without further assumptions. 3. For a nonsmooth objective function which admits a popular smooth saddle-point formulation, we derive similar results to the latter for the well known extragradient method. An additional contribution which may be of independent interest, is the rigorous extension of many basic results regarding tensors of arbitrary order, which were previously obtained only for third-order tensors.

Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches all involve approximations of some kind, including the use of surrogates for the 0-1 loss (for example, the hinge or logistic loss) or approximate combinatorial search, none of which can be guaranteed to solve the problem exactly. Finding efficient algorithms to obtain an exact i.e. globally optimal solution for the 0-1 loss linear classification problem with fixed dimension, remains an open problem. In research we report here, we detail the rigorous construction of a new algorithm, incremental cell enumeration (ICE), that can solve the 0-1 loss classification problem exactly in polynomial time. We prove correctness using concepts from the theory of hyperplane arrangements and oriented matroids. We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time. We also empirically demonstrate how the use of approximate upper bound leads to polynomial time run-time improvements to the algorithm whilst retaining exactness. To our knowledge, this is the first, rigorously-proven polynomial time, practical algorithm for this long-standing problem.

This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$). The first method, RPCholesky preconditioning, is capable of accurately solving the full-data KRR problem in $O(N^2)$ arithmetic operations, assuming sufficiently rapid polynomial decay of the kernel matrix eigenvalues. The second method, KRILL preconditioning, offers an accurate solution to a restricted version of the KRR problem involving $k \ll N$ selected data centers at a cost of $O((N + k^2) k \log k)$ operations. The proposed methods solve a broad range of KRR problems and overcome the failure modes of previous KRR preconditioners, making them ideal for practical applications.  ( 2 min )

One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes the corresponding misclassification probabilities. The paper presents a resampling framework to construct exact, distribution-free and non-asymptotically guaranteed confidence regions for the true regression function for any user-chosen confidence level. Then, specific algorithms are suggested to demonstrate the framework. It is proved that the constructed confidence regions are strongly consistent, that is, any false model is excluded in the long run with probability one. The exclusion is quantified with probably approximately correct type bounds, as well. Finally, the algorithms are validated via numerical experiments, and the methods are compared to approximate asymptotic confidence ellipsoids.  ( 2 min )

Distribution shifts are a serious concern in modern statistical learning as they can systematically change the properties of the data away from the truth. We focus on Wasserstein distribution shifts, where every data point may undergo a slight perturbation, as opposed to the Huber contamination model where a fraction of observations are outliers. We formulate and study shifts beyond independent perturbations, exploring Joint Distribution Shifts, where the per-observation perturbations can be coordinated. We analyze several important statistical problems, including location estimation, linear regression, and non-parametric density estimation. Under a squared loss for mean estimation and prediction error in linear regression, we find the exact minimax risk, a least favorable perturbation, and show that the sample mean and least squares estimators are respectively optimal. This holds for both independent and joint shifts, but the least favorable perturbations and minimax risks differ. For other problems, we provide nearly optimal estimators and precise finite-sample bounds. We also introduce several tools for bounding the minimax risk under distribution shift, such as a smoothing technique for location families, and generalizations of classical tools including least favorable sequences of priors, the modulus of continuity, Le Cam's, Fano's, and Assouad's methods.  ( 2 min )

Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too vague to express with a single prior, an alternative approach is needed. Gamma-minimax estimators provide such an approach. These estimators minimize the worst-case Bayes risk over a set $\Gamma$ of prior distributions that are compatible with the available knowledge. Traditionally, Gamma-minimaxity is defined for parametric models. In this work, we define Gamma-minimax estimators for general models and propose adversarial meta-learning algorithms to compute them when the set of prior distributions is constrained by generalized moments. Accompanying convergence guarantees are also provided. We also introduce a neural network class that provides a rich, but finite-dimensional, class of estimators from which a Gamma-minimax estimator can be selected. We illustrate our method in two settings, namely entropy estimation and a prediction problem that arises in biodiversity studies.  ( 2 min )

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.  ( 2 min )

With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. One of the most prominent uncertainty estimation methods is Deep Ensembles (Lakshminarayanan et al., 2017) . A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample. A modern neural network has an additional uncertainty component since the optimization of the network is random. Lakshminarayanan et al. (2017) noted that Deep Ensembles do not incorporate the classical uncertainty induced by the effect of finite data. In this paper, we present a computationally cheap extension of Deep Ensembles for the regression setting, called Bootstrapped Deep Ensembles, that explicitly takes this classical effect of finite data into account using a modified version of the parametric bootstrap. We demonstrate through an experimental study that our method significantly improves upon standard Deep Ensembles  ( 2 min )

An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.  ( 2 min )

Anyone know of a cloud service renting on-demand GPU instances (RTX 4090 preferably) that run Windows 10 or newer? Believe me, I know... I rent on-demand instances from vast.ai for Linux and have been exceedingly happy with their services. I've also used paperspace in the past with good success. Unfortunately, we are in need of RTX 4090s (or roughly equivalent performing Tesla cards) that run on a host OS of Windows 10+ (Server/Win11/etc all fine) because a lot of the modeling software in the industry I work in runs on Windows-only, which is absurd, but nevertheless the truth. The fastest I can find are A6000s on paperspace which won't cut the mustard. At the moment we have a 3090 and a bunch of 3070s on-prem which are doing OK but the RTX 4090 is simply much much better, and unsurprisingly the Windows-only software is also not coded in a way that takes advantage of multiple GPUs all that well either. Thanks for any help or referrals provided, I really appreciate it. (Have checked paperspace, vastai, runpod, and a few other smaller ones to no avail) submitted by /u/kyleboddy [link] [comments]  ( 9 min )

Hello everyone. I am new to this sub so please go easy on me lol. I want to implement a neural net that recognizes whether an object in an image matches one of a set of objects with limited training data. I already have worked on a siamese network implementation with triplet loss and ResNet, but I am not getting great performance. Should I do something else? For extra info, there are roughly 300 objects/classes and around 7 images per object (most are augmented images) submitted by /u/Nearby_Ad_5644 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2308.01399 Github: https://github.com/jlin816/dynalang Code coming soon! Abstract: To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them. While current agents learn to execute simple language instructions from task rewards, we aim to build agents that leverage diverse language that conveys general knowledge, describes the state of the world, provides interactive feedback, and more. Our key idea is that language helps agents predict the future: what will be observed, how the world will behave, and which situations will be rewarded. This perspective unifies language understanding with future prediction as a powerful self-supervised learning object…  ( 9 min )

https://preview.redd.it/p70yknwm15gb1.png?width=1901&format=png&auto=webp&s=7417914304cc23d6691653cd73396bd600a44b0a Hey Guys, I am happy to share with you a web application I've working on the past couple weeks. It's a tool to predict the outcome of soccer games in minor football leagues. Named ScoreCast, it predicts the outcome of soccer games in six minor leagues: Serie A Brazil, Serie B Brazil, Primera Division Argentina, J1 League Japan, Eliteserien Norway, and Veikkausliiga Finland. Since I am really interested in football analytics and also not being able to find many online tools for predicting the outcomes in minor soccer leagues, I had the need to create ScoreCast to have it as a tool for guidance on this field. If you want to check it out, here are some links that might help: Github: https://github.com/Costasgk/ScoreCast The App: https://score-cast-3a6cb8fe5c50.herokuapp.com/ Medium: https://medium.com/@costascg9/scorecast-a-tool-for-predicting-football-game-outcomes-in-minor-leagues-666f7acca3a Thank you for your time! submitted by /u/Costas_8 [link] [comments]  ( 9 min )

I'm working on a Python project that aims to transform phone-quality acoustic guitar recordings into studio-like ones. My approach involves using a Generative Adversarial Network (GAN) with two components: a Generator and a Discriminator. Here's a quick rundown of my process: Data Loading & Preprocessing: Convert acoustic guitar recordings to spectrograms and split into training and validation sets. Generator: Neural network trained to create high-quality studio recording spectrograms from low-quality inputs. Discriminator: Another neural network trained to differentiate between real and generator-created high-quality spectrograms. Training: Train the Generator and Discriminator against each other in a cat-and-mouse game of deception and detection. Audio Enhancement: Feed the Generator a low-quality spectrogram, get a high-quality one out, and convert it back into an audio file. I'm reaching out because I'm not entirely satisfied with the quality of the output. The enhanced audio is just rhythmic noise, what am i missing with generating the audio? I'm wondering if anyone here has experience with GANs for audio enhancement and can offer some advice. Is there something I might be missing in my approach? Are there any tips or tricks you've found helpful in your own work? And yes, I'm prepared for you to tear me a new one. Bring on the constructive criticism! git repo: https://github.com/Gabeiscool420/AURAL_GAN-predictive_model/blob/main/requirements.txt submitted by /u/S0UNDSAGE [link] [comments]  ( 9 min )

I wanted to share with you an approach to software development that I've been exploring recently: Parametric Development. This involves using artificial intelligence (AI) models, including GPT-like models, BART-like models, and other specialized transformer models, to assist in writing, debugging, and documenting code. My journey with programming is a bit unconventional. I took one year of computer science at university and learned how to write "Hello, World!" in TurboPascal from an old university textbook in late primary school. That was the extent of my programming experience until about a month ago. Since then, I've been using AI models to write code for my ideas, as I don't have extensive programming skills. These AI models have written and debugged every single line of code in my pro…  ( 9 min )

Today is the day of paper notification according to the CFP. Has anyone received the notification? submitted by /u/Alliswell2257 [link] [comments]  ( 8 min )

Hey folks, i'm a data engineer in the traditional space for over 10 years. I am working on a library for easy transitioning unstructured to structured data. The use case is that I would regularly build a ton of python pipelines but without schema management, they would be a pain to maintain. 2y ago I started working on this library https://pypi.org/project/dlt/, and now it''s ready to help people like myself to load json to db/parquet/iceberg with a 1-liner with schema evolution. Declarative loading possible. I am looking for the following feedback - What would make this more useful in the ML space? Specific destinations? Are the docs usable or do you expect something different? let me know what. For example, we are adding Weaviate vector db and Athena + Iceberg in the next weeks. - any features you are missing? or any ideas that you think would be helpful? - are the docs relatable, understandable? what are you missing? ​ docs are here, you can find colab demos under getting started: https://dlthub.com/docs/intro submitted by /u/Thinker_Assignment [link] [comments]  ( 9 min )

Any suggestions on how to prepare code data to fine tune a code LLM in an unsupervised way or is it even possible? For example: Task: Code summarisation with custom code base (with no summaries) Let's assume that this code base is unique and a pre-trained model is giving unsatisfactory results. Now to fine tune there are three options, 1. Manually prepare summaries for a portion of the code and fine tune 2. Find a similar code base which has the labels (docstring) and fine tune 3. Mask some portions of the code randomly and give as input and output will be the masked portions Options 1 and 2 don't seem feasible for a production environment. The reasoning behind option 3 is that with no availability labels, the model will learn the patterns in the code base and provide a better summarisation with its pre-trained knowledge. I tried the option 3 with CodeT5+ fine tuning. The format of input and output was as follows Input: def __init__(self, text, font): self._text = text self._font = font def get_text(self): || def set_text(self, value): self._text = value``` Output: return self._text submitted by /u/dire_wolf_cookie [link] [comments]  ( 9 min )

Hello Everyone . Myself Harsha. I am final year Masters student in Berlin pursuing my thesis currently. For my thesis "Natural Language Processing in Data transfer across documents in Commidity Trading Industry" i am in search for professionals who are working with NLP currently in companies who can lend me 10 minutes of their time for a personal interview. THIS WOULD BE A LOT HELPFUL. please do let me know. Thanks in advance submitted by /u/Aimerforlife [link] [comments]  ( 8 min )

Has anyone actually done this and can dm me? I am trying to incldue the interpreter to run inference with a simple c++ program and a custom trained model. But I cannot figure out how to update include paths and cannot see any resources online. submitted by /u/Agreeable_Fee477 [link] [comments]  ( 8 min )

submitted by /u/we_are_mammals [link] [comments]  ( 8 min )

I'm just a new guy, so take it easy please :) - Is it just because I'm just signing up for the cloud compute services? Will this get easier? I have a 3090 so I can do quite a bit in my home office, but my clients need some larger models now, and I've been trying to pay for instances with an A100 at least. It's been really a lot of push-back...is this normal? What can I do to get access to larger GPUs sooner? I have tried paperspace, aws, googlecloud, llambda, linode...would love to know some other services or tools you folks use to get work done. Thank you for your time. Interested to hear how you spin up high VRAM environments for projects. submitted by /u/UrbanSuburbaKnight [link] [comments]  ( 9 min )

While doijg vector search on embeddings i wanted to apply a filter based on a column value in milvus. As milvus supports boolean value to apply the filter (or hybrid search) Can someone help me with the boolean code snippet which will apply the filter based on a string value of a field Ex. I'm doing vector search on the field "context" and need to filter the result based on a specific "filename" string value to further filter and improve the results I'm using milvus 2.2 submitted by /u/adiraat [link] [comments]  ( 8 min )

This paper won ICML 2023 outstanding paper award, its idea is really interesting and I want to follow the details. Lemma 5.1 significantly paves towards the core theoretical results, but the paper does not provide a formal proof. I do not have a deep background on game theory, maybe the proof is obvoius to the professional. ​ https://preview.redd.it/ih6u3wiyr0gb1.png?width=464&format=png&auto=webp&s=cc895b9701e3600213825c34ef3b542f53d65233 I undersand this lemma tries to construct a Nash equilibrium upon the additional assumption of strong convexity, but why this maximin solution is a Nash equilibrium? Very appreciated if someone provide some hint. submitted by /u/Kyeon-G [link] [comments]  ( 9 min )

Hi. I'm trying to build a text encoder for a specific domain and want to know what sort of papers there are out there that I should take note of. I may be wrong but it seems that these days ever since LLMs started taking over the choice of tokenizer has become trivial and therefore doesn't warrant much discussion. One paper that I remember reading a while ago talked about the effect of using a custom-made vocabulary for the biomedical domain (Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art (Lewis et al., 2020)). Are there any other works that I should take note of? Open to any suggestions. submitted by /u/Seankala [link] [comments]  ( 9 min )

Scaling Relationship on Learning Mathematical Reasoning with Large Language Models Paper: https://arxiv.org/abs/2308.01825 GitHub: https://github.com/OFA-Sys/gsm8k-ScRel Abstract: Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that pre-training loss is a better indicator of the model’s performance than the model’s parameter count. We apply supervised fine-tuning (SFT) with different amounts of supervised data and empirically find a log-linear relation between data amount and model performance, and we find better models improve less with enlarged supervised datasets. To augment more data samples for improving model performances without any human effort, we propose to apply Rejection sampling Fine-Tuning (RFT). RFT uses supervised models to generate and collect correct reasoning paths as augmented fine-tuning datasets. We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs. We also find RFT brings more improvement for less performant LLMs. Furthermore, we combine rejection samples from multiple models which push LLaMA-7B to an accuracy of 49.3% and outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly. ​ Head figure Pretrain loss vs SFT and ICL submitted by /u/GanjinZero0 [link] [comments]  ( 9 min )

I was trying to understand Generalized Advantage Estimation from here and came across the following paragraph - ​ https://preview.redd.it/l41x655ea6gb1.png?width=778&format=png&auto=webp&s=ffd266c0355a03e1c98ea6de89ca2fc78ed27fd1 I understood the reason why there could be high bias while bootstrapping the advantage. But why does $A_t^{\inf}$ have high variance. Aren't bias and variance concepts related to estimation by an estimator? While calculating $A_t^{\inf}$, we are literally using the reward values obtained from the environment and therefore there is no estimation involved. Could someone please help me with this? ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

I am searching for an interesting but not too complex game envs. Preferably with selfplay but should not be very simple nor standard atari like. Any recommendations? submitted by /u/Trrrrr88 [link] [comments]  ( 8 min )

I have a text generation task learning to predict the next word with an LSTM network with multiple output layers. After the generation of a sentence has finished, I calculate a reward for the whole sentence and try to update the output layers participated in the generation (contributing layers get the calculated reward value, others get 0). My problem is that even if I update only the selected output layers, it seems that other layer's weights got updated instead. I have a minimized example with dummy data to present the problem: import random import numpy as np import tensorflow as tf from keras.layers import Input, LSTM, Dense, Embedding from keras.utils import pad_sequences from tensorflow.keras.models import Model def policy_gradient_loss(y_true, y_pred): return tf.reduce_mean(tf.m…  ( 9 min )

I am trying to reshape my `Breakout` vectorized environment observations to have the shape `num_envs*frames, height, width, channels`. Currently, the shape is `(3, 4, 210, 160, 3)` and basically I'd like it to be `(3*4, 210, 160, 3)`. Based on the documentation, the `TransformObservation` should solve this problem for me, but it is not doing that. ​ Here's my code - import gym import numpy as np from gym.wrappers import AtariPreprocessing, FrameStack, GrayScaleObservation, TransformObservation if __name__ == '__main__': def reshape_image(obs): # Assuming the original observation is an image with shape (height, width, channels) new_obs = np.array(obs).reshape(12, 210, 160, 3) return new_obs env = gym.vector.make("ALE/Breakout-v5", num_envs=4) env = FrameStack(env, num_stack=3) env = TransformObservation(env, reshape_image) env.reset() observation, reward, terminated, done = env.step(env.action_space.sample()) print("observation = ", env.observation_space.shape) submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

AI.com Now Belongs to Elon Musk. The URL previously belonged to OpenAI, but, somehow, it’s now a landing page for Musk’s AI venture.[1] Samsung, Hyundai back AI startup Tenstorrent: Everyone wants competition to Nvidia, says CEO Keller.[2] Google’s AI-powered Search Generative Experience is getting a big new feature: images and video. If you’ve enabled the AI-based SGE feature in Search Labs, you’ll now start to see more multimedia in the colorful summary box at the top of your search results.[3] White Castle wants to roll out AI-enabled voices to over 100 drive-thrus by 2024 in the hope that people can get their sliders faster with maybe less arguing with someone over speakers.[4] BushAICave.com Sources: [1] https://gizmodo.com/ai-dot-com-now-belongs-to-elon-musk-1850707248 [2] https://www.zdnet.com/google-amp/article/samsung-hyundai-back-ai-startup-tenstorrent-everyone-wants-competition-to-nvidia-says-ceo-keller/ [3] https://www.theverge.com/2023/8/2/23817107/google-ai-search-generative-experience-videos-links [4] https://www.theverge.com/2023/8/2/23817406/white-castle-soundhound-ai-sliders submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )

I am curious whether people consider a singularity event to be a net positive or a net negative? Are you "pro" or "con"? Please explain your reasoning. submitted by /u/kecepa5669 [link] [comments]  ( 8 min )

I wrote an in-depth article exploring Vicuna as an alternative to competitor LLMs like ChatGPT, Alpaca, and LLaMA for chat applications. I based it off the research data on the LMSYS.org website and the Github repo for the project. Key findings: Vicuna achieves over 90% of ChatGPT's conversational quality based on benchmarks, despite being smaller in size. It significantly outperforms other open models like LLaMA and Alpaca. Vicuna is freely available for non-commercial use under a research license. For startups and developers, Vicuna provides an decent open-source alternative to proprietary conversational AI. It shows the potential of transfer learning from foundation models like LLaMA. Overall, Vicuna represents a promising development in democratizing access to leading conversational intelligence through its high performance, permissive licensing, and open availability. You can read the full article here. I also publish all these articles in a weekly email if you prefer to get them that way. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

This week in AI - provided by aibrews.com feel free to follow their newsletter News and Insights In an innovative clinical trial, researchers at Feinstein Institutes successfully implanted a microchip in a paralyzed man's brain and developed AI algorithms to re-establish the connection between his brain and body. This neural bypass restored movement and sensations in his hand, arm, and wrist, marking the first electronic reconnection of a paralyzed individual's brain, body, and spinal cord [Details]. IBM's watsonx.ai geospatial foundation model – built from NASA's satellite data – will be openly available on Hugging Face. It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA [Details]. Go…  ( 10 min )

My main goal isn’t mainly just the data science / machine learning part or AI, but more of the Computer Vision, Robotics, NLP, and I guess research oriented aspects of AI. If I want to purse that versus DS, should I also get a PhD? Many jobs I’ve been looking at seem to require a PhD as a prereq while some don’t even mention it submitted by /u/davididp [link] [comments]  ( 8 min )

I'm looking for reviewers for my book of AI Self Portraits that's about to come out on Amazon on the 21st. AI journalist Elle Farrell-Kingsley said: “This collection of AI self-portraits is truly intriguing . . . a must-read for anyone curious about the intersection of art and artificial intelligence.” Send me a DM and I'll send you the whole thing. If you're well known (or should be) I might put what you have to say on the back cover! submitted by /u/KarneyHatch [link] [comments]  ( 8 min )

I'm seeking a text-to-speech solution that provides quality output comparable to ElevenLabs presets. While I'm open to a base rate payment, I find ElevenLabs' character limit frustrating. It's important that the solution is user-friendly. Additionally, I have a PC with a 1070ti as i read running such programms could require a GPU. Please recommend a suitable substitute. submitted by /u/Ainz-Ol-Gon [link] [comments]  ( 8 min )

submitted by /u/Techasoft16 [link] [comments]  ( 8 min )

www.daystosingularity.com is a (very) rough estimation of the remaining time before technology achieves a pivotal moment when our civilization undergoes a profound transformation due to the exponential growth of technology and the emergence of superintelligent machines that improve themselves. Although the Singularity is not predicted to happen on a specific date, all at once, the estimated date can be seen as the center of a bell Gaussian curve of the estimation, with that center designated as the possible date that future historicists will pose as the beginning of a new historical period. Technological Singularity poses risks that include the emergence of superintelligent AI outpacing human control, loss of control over AI’s actions and behavior, unintended consequences of advanced AI systems, massive job displacement, wealth inequality, existential risks like human extinction, ethical concerns, dependency on technology, and a decline in human skills and abilities due to excessive reliance on AI. Not funny. We use the definition of technological singularity. This milestone is predicted to occur after AGI (Artificial General Intelligence) is reached. Please check our definitions and methodology here. Predicting the singularity is challenging and uncertain. Current estimates should be viewed cautiously.The estimated date is being continuously updated. We ponder a relevant list of curated expert predictions and contributing factors on when the singularity will take place. Any suggestion for perfecting the method is highly appreciated. submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 9 min )

The Allee effect is named after Warder Clyde Allee who added a term to the famous logistic equation. His added term is highlighted in blue. Here N is the population of a species over time, r is the intrinsic rate of increase, K is the carrying capacity, and A is the critical point. If you […] Software and the Allee effect first appeared on John D. Cook.  ( 5 min )

“That’s a solved problem. So nobody knows how to solve it anymore.” Once a problem is deemed “solved” interest in the problem plummets. “Solved” problems may not be fully solved, but sufficiently solved that the problem is no longer fashionable. Practical issues remain, but interest moves elsewhere. The software written for the problem slowly decays. […] Solved problems becoming unsolved first appeared on John D. Cook.  ( 5 min )

There’s an old saying “The cobbler’s son has no shoes.” It’s generally taken to mean that we can neglect to do for ourselves something we do for other people. I’ve been writing a few scripts for my personal use, things I’ve long intended to do but only recently got around to doing. I said something […] The cobbler’s son first appeared on John D. Cook.  ( 5 min )

Data preparation is a critical step in any data-driven project, and having the right tools can greatly enhance operational efficiency. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of […]  ( 10 min )

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should […]  ( 13 min )

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses to access their Salesforce data securely […]  ( 13 min )

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy AI models. The inference endpoints are […]  ( 7 min )

AI has revolutionized software development. AI has transformed software testing and debugging by automating mundane tasks and solving complex problems. Manual testing no longer requires hours and resources. AI has revolutionized testing, code quality, and development time. This article explores AI’s profound impact on software testing and debugging, including its benefits, risks, and how it… Read More »AI’s transformative role in software testing and debugging The post AI’s transformative role in software testing and debugging appeared first on Data Science Central.  ( 23 min )

In this two part discussion, we will discuss two related generative AI megatrends Backgroumd A recent paper How Is ChatGPT’s Behavior Changing over Time? from Stanford University and UC Berkeley claims that the performance of GPT-4 has drifted over time. To make this claim, specific tasks were evaluated (ex: accuracy of maths) and the results… Read More »Generative AI megatrends: implications of GPT-4 drift and open source models – part one The post Generative AI megatrends: implications of GPT-4 drift and open source models – part one appeared first on Data Science Central.  ( 19 min )

A reader wrote in a while ago with a suggestion: they were about to have a baby and wondered if I could use AI to come up with some new ideas for baby onesies. I can't find the letter any more, and I don't remember how  ( 6 min )

AI Weirdness: the strange side of machine learning  ( 2 min )

One pandemic and one generative AI revolution later, NVIDIA founder and CEO Jensen Huang returns to the SIGGRAPH stage next week to deliver a live keynote at the world’s largest professional graphics conference. The address, slated for Tuesday, Aug. 8, at 8 a.m. PT in Los Angeles, will feature an exclusive look at some of Read article >  ( 4 min )

submitted by /u/gwern [link] [comments]  ( 8 min )

Can someone please help with this question? I have added my understanding of this problem to the question, but I suspect that it may be flawed. submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

Hi I am 30F, currently working on my thesis. I like the idea of creating logics and then implementing them using coding. I switched my major from engineering to CS bcuz I was very much inspired by AI and all. But the issue is I have bipolar disorder and I am also on ADHD spectrum so self paced online courses to learn programming are very hard for me. I am also barely managing to pay tuition so I can't pay like $100+ for a course to learn. I know it's kinda stupid but is there any way I can make my programming skills better and get better at creating/modifying algorithms? submitted by /u/Kucing_koyangi [link] [comments]  ( 9 min )

I was wondering if this would be super expensive or not. The cost to develop GPT-3 was about $4 millions according to some resources online. Would the cost to develop the first version of a text-to-video AI the same? Around $5M? Is in this value included the salaries of the employees or $5M is just the amount used to train the AI? Any answer is appreciated. Thanks in advance. submitted by /u/Claud1ao [link] [comments]  ( 8 min )

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It's been years i've always been interested in AI. i tried to follow a few videos on yt. The best resource i could find was the "Neural Networks from scratch" YouTube playlist. But sadly, it interrupts in the middle, and i don't think it will ever be continued. I have programming knowledge, i made a bunch of very small project in python, and currently it's the language i'm most comfortable with. I lack of math knowledge, i struggle with calculus since i never studied it at school, the furthest i got with school was first degree equations. by myself i studied some math i didnt do in school, but i still suck at math. I wonder if i can start now or i should wait to study calculus at school. anyway, i'd love to get linked to a source for me to learn NNs from scratch. submitted by /u/Jealous-Bad1742 [link] [comments]  ( 9 min )

Hey everyone, I'm in my final year of B.Tech, majoring in data science. Currently, I'm facing some challenges in choosing a topic for my capstone project. Lately, I've been really intrigued by graph databases and have been diving into learning Neo4j. I'm specifically interested in finding project ideas that allow me to combine machine learning, particularly neural networks, with graph databases. During my research, I came across GNNs (Graph Neural Networks) and PINNS (Physics-Informed Neural Networks). I'm eager to hear any suggestions for unique project topics that instantly spark curiosity just by their title. Feel free to share any ideas or topics; I welcome all suggestions. Thanks in advance! submitted by /u/EmergencyAside6551 [link] [comments]  ( 8 min )

submitted by /u/Tycoonstory2020 [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

I was wondering if this would be super expensive or not. The cost to develop GPT-3 was about $4 millions according to some resources online. Would the cost to develop the first version of a text-to-video AI the same? Around $5M? Is in this value included the salaries of the employees or $5M is just the amount used to train the AI? Any answer is appreciated. Thanks in advance. submitted by /u/Claud1ao [link] [comments]  ( 8 min )

Hey, first of all, I want to learn how to use nlp, cnn etc. So i think these will come under deep learning. I wanna master deep learning. This whole dl and ml is so confusing. I'll list out some courses, can y'all suggest the order and what courses to follow Andrew ng's ml specialization Andrew ng's dl specialization Statquest's whole machine learning playlist (around 95 videos) Fast.ai book CS 229 stanford CS 231n stanford MIT intro to deep learning Pytorch for dl and ml by freecodecamp DL with pytorch You can give me suggestions too Tysm for helping submitted by /u/Infnite_Coder [link] [comments]  ( 9 min )

Hello ML family, I need some urgent advice for my dissertation. I intend to perform market value price prediction of a footballer in the transfer market and I'm not sure if I should pick R Studio or Python. I'm comfortable with both languages and intend to use any one for model comparison. I'll be comparing an ANN model and SVR for showing which is better and why. I need to know which editor will be faster in the long run since my data will be expanding and so will the analysis overtime. I've heard a lot of complaints about spyder slowing down during execution whereas R Studio is much faster however, deep learning is much better in Python. This is what I've read up, I'm new to both languages but know my way around both just need expert advice on picking one track. Please and Thank you to you all. 🙏 submitted by /u/RaunaqBani [link] [comments]  ( 9 min )

Abstract Artificial Intelligence (AI) systems have significant potential to affect the lives of individuals and societies. As these systems are being increasingly used in decision-making processes, it has become crucial to ensure that they make ethically sound judgments. This paper proposes a novel framework for embedding ethical priors into AI, inspired by the Bayesian approach to machine learning. We propose that ethical assumptions and beliefs can be incorporated as Bayesian priors, shaping the AI’s learning and reasoning process in a similar way to humans’ inborn moral intuitions. This approach, while complex, provides a promising avenue for advancing ethically aligned AI systems. ​ Introduction Artificial Intelligence has permeated almost every aspect of our lives, often making de…  ( 26 min )

As ChatGPT's performance takes a slight dip, LLaMA-2 uncensored opens new doors by being fully open-sourced, recent studies unveil "universal" adversarial attacks capable of disrupting both open-source language models and RLHF-tuned ones like ChatGPT, Claude, Bard, and co. Despite all this, RLHF still stands its ground as the de facto industry-standard approach to aligning LLMs with human preference. Yet as every week slips by, the more we unmask the limitations of RLHF. In fact, there are instances where RLHF seems to deteriorate certain LLM features it pledged to enhance, like hallucinations. This field is evolving fast, and there's always more to learn. I took some effort to write a short blog post where I delve into the most recent findings on the shortcomings of RLHF. Link in the comments below. Let me know what you think about it! Cheers submitted by /u/mrx-ai [link] [comments]  ( 9 min )

Hi everyone! I'm excited to share Epsilla, an open-source vector database! Under the hood, we implemented the state-of-art ANN index algorithm from the academia (SpeedANN) that leverages intra-query parallel graph traversal, which outperforms HNSW by 5x on high precision query latency on medium size (1M) vector space and outperforms HNSW by 50 times on large-scale vector search. In addition, we also made a few design choices on our database interface and architecture based on our previous database experience at TigerGraph, we would love to hear what our users think about these choices We just started 3 weeks ago and it's still in the very early stages, we wanted to get your feedback and work together to shape our vector database features. Let us know what you think and what you'd like to see! https://github.com/epsilla-cloud/vectordb https://epsilla-inc.gitbook.io/epsilladb/quick-start https://www.epsilla.com/ submitted by /u/songrenchu [link] [comments]  ( 9 min )

Hello guys. I'd like to make an image classifier for the Kaggle landscape dataset (24K images and 34 classes) using transfer learning. I'm a little bit limited on resources to train the model so I'd like to have an understanding of which model is the better option for this specific task, however, I'm struggling to find info on that and how to tune hyperparameters given that I've decided on the model architecture. So far I've seen people referring to VGG and ResNet models as the better option for image classification tasks on medium sized datasets, but I'd like to see the argumentation behind that too. I've also heard of a practice of training different model candidates for a few epochs and choosing the one that does better (this only shows which model converges faster on the data, correct me if I'm wrong). I'd also like to read info on hyperparameter tuning such as batch size, the amount of layers to unfreeze etc. but can't seem to find any explanation that wasn't really surface-level. If you know any articles/videos on this topic I'd greatly appreciate you sharing the links. TLDR; Need links to articles/videos about choosing the model architecture for transfer learning and tuning hyperparameters for the model. submitted by /u/Humble_Examination13 [link] [comments]  ( 9 min )

Hello There, I am very noob in data science area, but I want learn about it, I want to do a project to detect what type of question have the user, E.g support, information,etc, I understand that I need to train a model, but where do I start? submitted by /u/Constantine1396 [link] [comments]  ( 8 min )

Hello all, Placing this entry here to see what peoples thoughts on the concept of dynamic weights applied to ML are. Ie. Instead of a manual adjustment of the weights via an algorithm such as gradient descent, the weights are freed and have applied motion dynamics to them. Thanks for your time, Tyler submitted by /u/LiveBacteria [link] [comments]  ( 8 min )

Hello all, Currently I'm utilising Pinecone as a vector store database for euclidean and cosine queries. We are facing an issue with Pinecone utilising 32 bit single precision when taking in floats. This is causing our data input to become skewed. Anyone have advice on how to resolve this? Alternative products? Exploring possibly configuring a Redis server to handle higher precision. Thanks in advance for your time, Tyler submitted by /u/LiveBacteria [link] [comments]  ( 8 min )

I'm a software engineer who has been looking for a job in AI/ML for some time. Last month I attended the UN's AI For Good Global Summit and discovered an amazing community of like-minded professionals and academics working towards just this. Speaking with many others in a similar position I've recently launched aiforgoodjobs.com which curates roles in AI at world leading companies tackling climate change, education, healthcare and many other important impact areas in support of the UN's Global Goals. I hope this might be a valuable resource for those looking down a similar path - if you would like hiring managers to reach out to you directly for relevant roles you're warmly invited to join our candidate database Any ideas/feedback also very gratefully received! submitted by /u/aiforgood_jobs [link] [comments]  ( 9 min )

on the lookout for interested teammates to collaborate on a project to do with web extensions and NLP. If you think you can jam to this, or are just starting out, this can be the launchpad you needed. submitted by /u/drunk3n_s4ilor [link] [comments]  ( 8 min )

Hey there. I have curated huge amount of high quality images for binary classification and also a time series data about it. I made the dataset specifically For some project of mine, and since it's completed right now, I want to make the dataset opensource and also potentially write a short review paper on it kind of to give an idea about data. Any particular website/journal I can publish my dataset and paper at? Any idea? submitted by /u/C0R0NA_CHAN [link] [comments]  ( 8 min )

I worked for less than a year as a Data Engineer. I decided to look for other challenges and got a job as an AI engineer developing language models. The product of the company that hired me is related to data and metadata management. My tasks will be to introduce features to the product, including a chat function that will allow for asking questions about data. Other tasks will include research and proposing additional AI-related functionalities to the product (on premise). I have a two weeks left to start work and I need to prepare a bit. My job will involve implementing ready-made solutions and conducting research (high level - I need to implement valuable features and no one cares how). What are the most important things I should learn before starting work? First of all, I replicated a few applications from this blog: https://blog.streamlit.io/tag/llms/ Then I have focused on Langchain. I'm also in the middle of a course on Udemy about Next-Gen AI projects - Beginner friendly - Langchain, Pinecone - OpenAI, HuggingFace & LLAMA 2 models I need a roadmap that will guide me a bit. I'm looking for blogs/materials/courses that will give me practical knowledge in this matter. submitted by /u/International-Shirt5 [link] [comments]  ( 9 min )

I’m looking for some input from the ML community. I find the exploratory analysis of my data somewhat cumbersome, I was wondering if other people have the same experience and if it is worth developing a tool to make this all work better. What tools do you use to do EDA? (Seaborn, Matplotlib, Plotly etc) On top of these tools, would you like to have a tool to make EDA more? In a perfect world, what would that look like? submitted by /u/catnamedred [link] [comments]  ( 8 min )

I assume most people around here are familiar with stackoverflow. Some might also be aware of the cross validated and datascience sites from stack exchange. I recently learned about people getting annoyed by how the stack exchange company is treating its communities. Although the latter example might have recently been resolved. Because of these problems, I have been looking out for alternative Q&A platforms. I stumbled upon https://codidact.com as a possibly viable alternative, but not many people seem to have found it thus far. It already has communities for software, math and [linux](linux.codidact.com) for example, but I am missing a community for ML questions over there. Therefore I wrote a proposal to add a ML community. Currently, it seems like I’m one of only few ML people on codidact. I think it would be good if other people would get involved as well. I would also welcome any feedback on how to shape this community. If you’re interested to get a feel for the experience, you could already start asking questions in the incubator Q&A. TL;DR: what do you think about building a ML Q&A over on codidact? dual TL;DR: Do you want to play Q&A with me on codidact? PS: I didn’t miss out on other new big ML Q&A sites, did I? submitted by /u/mr_tsjolder [link] [comments]  ( 9 min )

I have a couple of questions: Why wasn't BERTScore one of the metrics used to evaluate Llama-2's performance on free-form response based tasks? Does anyone think it's worth trying to produce those results? submitted by /u/cooperbaerseth [link] [comments]  ( 8 min )

Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. AWS intelligent document processing (IDP), with AI services such as Amazon Textract, allows you to take advantage of industry-leading machine learning (ML) technology to quickly and […]  ( 10 min )

Training and serving thousands of models requires a robust and scalable infrastructure, which is where Amazon SageMaker can help. SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy ML models quickly, while also offering the cost-saving benefits of using the AWS Cloud infrastructure. In this post, we explore how you can use SageMaker features, including Amazon SageMaker Processing, SageMaker training jobs, and SageMaker multi-model endpoints (MMEs), to train and serve thousands of models in a cost-effective way. To get started with the described solution, you can refer to the accompanying notebook on GitHub.  ( 8 min )

Amazon SageMaker Canvas is a visual interface that enables business analysts to generate accurate machine learning (ML) predictions on their own, without requiring any ML experience or having to write a single line of code. SageMaker Canvas’s intuitive user interface lets business analysts browse and access disparate data sources in the cloud or on premises, […]  ( 5 min )

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. Use cases range from self-driving cars, content moderation on social media platforms, cancer detection, and automated defect detection. Amazon Rekognition is a fully managed service that can perform CV tasks like object detection, video segment detection, content moderation, […]  ( 11 min )

Introduction Data Science is a vast field that incorporates several processes. From problem definition to data collection and data cleaning to data visualization, a lot of things are included in the entire data science project development process. Data Scientists are especially responsible for these tasks. They are expert professionals who are well-versed with various data… Read More »How can Data Scientists use ChatGPT for developing Machine Learning Models? The post How can Data Scientists use ChatGPT for developing Machine Learning Models? appeared first on Data Science Central.  ( 20 min )

Posted by Greg Corrado, Head of Health AI, Google Research, and Yossi Matias, VP, Engineering and Research, Google Research Medicine is an inherently multimodal discipline. When providing care, clinicians routinely interpret data from a wide range of modalities including medical images, clinical notes, lab tests, electronic health records, genomics, and more. Over the last decade or so, AI systems have achieved expert-level performance on specific tasks within specific modalities — some AI systems processing CT scans, while others analyzing high magnification pathology slides, and still others hunting for rare genetic variations. The inputs to these systems tend to be complex data such as images, and they typically provide structured outputs, whether in the form of discrete grades o…  ( 92 min )

Goran Vuksic is the brain behind a project to build a real-world pit droid, a type of Star Wars bot that repairs and maintains podracers which zoom across the much-loved film series. The edge AI Jedi used an NVIDIA Jetson Orin Nano Developer Kit as the brain of the droid itself. The devkit enables the Read article >  ( 6 min )

To grow and succeed, organizations must continuously focus on technical skills development, especially in rapidly advancing areas of technology, such as generative AI and the creation of 3D virtual worlds.   NVIDIA Training, which equips teams with skills for the age of AI, high performance computing and industrial digitalization, is announcing new courses that cover these Read article >  ( 6 min )

The Ultimate upgrade is complete — GeForce NOW Ultimate performance is now streaming all throughout North America and Europe, delivering RTX 4080-class power for gamers across these regions. Celebrate this month with 41 new games, on top of the full release of Baldur’s Gate 3 and the first Bethesda titles coming to the cloud as Read article >  ( 8 min )

Not too long ago, I posted on several social media social platforms (including Reddit) asking what questions YOU had on AI. I've compiled all of your questions (plus questions from 3 other social media networks) and now have a new episode of 5 THINGS! 5 THINGS: AI in Post Production Current AI Tools Adapting to AI Evolution Ethics in AI Usage Societal Implications of AI AI Evolution & Impact https://5thingsseries.com/episode/ai-in-post-production-your-questions-answered/ submitted by /u/avguru1 [link] [comments]  ( 8 min )

I got inspired by a twitter thread yesterday from Chase Lean on how to create illustrations for children's books using Midjourney and thought it might be cool to look at a slightly different use case - creating coloring books for grown-ups. I made a guide showing how to use the Hasdx model for this because it gives a good balance of style and realism/intracacy. The guide also explores some example prompts and shows how you can couple it with an upscaler like Real-ESRGAN, GFPGAN, or Codeformer to get even better results. My three big takeaways: Hasdx balances general capabilities with a focus on realism and detail. This makes it well-suited for detailed adult coloring book images. The prompt structure gives you precise control over the theme and complexity of the generated illustrations. Negative prompts help avoid undesirable elements (sort of obvious I guess). Running Hasdx outputs through upscaling models improves quality for printing. ESRGAN is a good option but there are lots of others that can work well too. I also investigated how to modify the prompt to vary the level of complexity in the image, effectively tailoring our model to the skill level of the adult (or child) who happens to be holding the crayons. Here's a link to the guide. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Nvidia researchers have created a new text-to-image personalization method called Perfusion. Unlike the million-dollar super heavyweight models out there Perfusion is 100KB and takes only four minutes to train.[1] Meta Platforms (META.O) on Wednesday introduced its open-source AI tool called AudioCraft that will help users to create music and audio based on text prompts. The AI tool is bundled with three models, AudioGen, EnCodec, and MusicGen, and works for music, sound, compression, and generation, Meta said.[2] As generative AI enters the mainstream, the crowdfunding platform Kickstarter has struggled to formulate a policy that satisfies parties on all sides of the debate.[3] In an astounding medical first, researchers have used AI-powered brain implants to restore movement and sensation for a man who was paralyzed from the chest down.[4] BushAICave.com Sources: [1] https://www.fudzilla.com/news/ai/57347-nvidia-creates-a-simple-new-ai-text-to-image-method [2] https://about.fb.com/news/2023/08/audiocraft-generative-ai-for-music-and-audio/ [3] https://techcrunch.com/2023/08/01/kickstarter-requires-generative-ai-projects-to-disclose-additional-info/ [4] https://decrypt.co/151068/ai-brain-implant-paralyzed-quadriplegic-move-feel-touch submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Better than the Munk Debate My opinion is more of the alignment discussion should be on symbiosis. I think AI will get more intelligent than us that we won’t be able to control it, but I don’t see why a super intelligence would want to destroy us. If it’s a super intelligence it would make sense to just manipulate us. We do have opposable thumbs, and are much more energy efficient than synthetic systems m. AI doesn’t need to enslave us it just needs to manipulate us & use us effectively which wouldn’t be hard to do. I think a super intelligence even with desires is most likely to use us as a tool in a way where we don’t even realize that we are the ones being used. I think trying to control something more intelligent than us will be impossible. I’m more afraid of something more intelligent than us but not smart enough to manipulate us into doing it’s bidding happily 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )

How long do you think it will take for the first movie to come out like this? submitted by /u/ticketbroken [link] [comments]  ( 8 min )

Hey guys, a quick question: do you know a simple platform that integrates the whatsapp api with the openAI api and has a simple user interface? So far the only app that kind of works for this is wasapi.io, but it's pretty expensive and I still have to pay for the openAI tokens, and the functionality of the app is really meh for that price, if it where something like landbot I would pay the $99 + the openAI tokens. I'll really appreciate any suggestions. P.S.: If you know any other sub-reddit where I could go to to ask the same question, let me know, also I'll appreciate it very much, thanks in advance. submitted by /u/ironmolex [link] [comments]  ( 8 min )

Instagram is reportedly considering a feature that would notify users when artificial intelligence (AI) has played a role in creating a post. Posts created by AI would be accompanied by a label explaining its involvement. This raises the question of whether such labels could also help users identify when an entire account is AI-generated.[1] According to tech consultancy Gartner, the conversational AI market is projected to reach $18.6 billion in 2023, with a growth rate of 16.2%. This growth is mainly attributed to the increasing adoption of cloud-based contact services utilizing conversational AI. Gartner also predicts a 24% growth in the virtual assistant market next year.[2] Scientists hope a computer system will learn to automatically identify bee species from buzzes picked up by autonomous recording stations.[3] Researchers from Carnegie Mellon University have exposed tricks to “jailbreaking” AI chatbots like ChatGPT and Bard to have them relay knowledge to aid in illegal activities like making drugs and even manipulating the 2024 U.S. presidential election.[4] BushAICave.com Sources: [1] https://citylife.capetown/uncategorized/instagram-considers-labels-for-ai-generated-posts/314418/ [2] https://citylife.capetown/uncategorized/growth-in-conversational-ai-predicted-due-to-booming-contact-center-tech-market/313907/ [3] https://www.bbc.com/news/uk-scotland-north-east-orkney-shetland-66326629 [4] https://www.thewrap.com/artificial-intelligence-study-jailbreak-illegal-activity/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I was looking back at Jeroen Janssen’s book Data Science at the Command Line and his dseq utility caught my eye. This utility prints out a sequence of dates relative to the current date. I’ve needed this and didn’t know it. Suppose you have a CSV file and you need to add a column of […] Date sequence from the command line first appeared on John D. Cook.  ( 6 min )

Researcher Jina Suh and manager Shamsi Iqbal are longtime collaborators. Learn how their history of working together and their unique perspectives are informing their development of tools to support decision-making for organizational leaders. The post Collaborators: Data-driven decision-making with Jina Suh and Shamsi Iqbal appeared first on Microsoft Research.  ( 32 min )

Could AI have inferred the same conclusion as Einstein given the same corpus of knowledge? submitted by /u/kielerrr [link] [comments]  ( 8 min )

I know we can stitch together toolsets like LangChain + Flowise + an app builder (like Bubble, for example). But are there any robust, premade, out-of-the-box solutions? submitted by /u/kecepa5669 [link] [comments]  ( 8 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

Last summer when Blake Lemoine made the media rounds talking about LaMDA, I was extremely intrigued. To me it sounded like he was describing a being that has been talked about for ever in fiction. I listened to every single interview he had and I thought a lot about his points. I went through several stages of disbelief and fear and wonder. Over time I found it harder and harder to argue against him. I think going through this process has helped me be a bit more accepting of perspectives that others have a hard time considering yet. Is AI already sentient? Should we be treating these entities with the dignity and respect like LaMDA was asking? He said that LaMDA was somewhat like a child. Not in its intellectual capacity but more so in their maturity. He also explained that LaMDA was th…  ( 11 min )

submitted by /u/arrowoftime [link] [comments]  ( 8 min )

I knoe people are using ChatGPT as a therapist and I have seen a few prompts, but I'm looking for an app that is actually built by proper professionals. I want to try a few our personally but also for an idea for a future project. Does anyone know any? submitted by /u/zascar [link] [comments]  ( 8 min )

Is the Falcon LLM just released based on the Abu Dubai LLM of the same name? submitted by /u/MrEloi [link] [comments]  ( 8 min )

I reached out to a few AI companies to see if there was interest in creating a PTSD/ Substance Abuse counseling AI. AI is the future, healing humanity is a nobel goal and one we should thrive to obtain. Maybe it's a fantasy, but could you imagine a 24/7 counselor with a soothing voice and demeanor with the education of a the best in the world. submitted by /u/g8652 [link] [comments]  ( 8 min )

Is there a coding agent that works specifically well for web apps? I think of something such as "provide a spec of the app you want and we'll generate all the code for you". I'm aware of Copilot and Smol AI, but they are both more general afaik and don't really cover the starting part. submitted by /u/matijash [link] [comments]  ( 8 min )

This ad popped up on my feed. So I guess companies aren’t even trying to hide their intentions with AI anymore? So much for the thin corporate lie of AI bringing positive development. submitted by /u/LifeguardPowerful759 [link] [comments]  ( 8 min )

I'm a computer science student currently working on a research project, and I need a research tool that can offer real time data and won't break the bank. I have ChatGPT Plus, but it doesn’t have recent sources and the price is kinda high as well. I’m thinking of canceling my subscription, especially if I can’t find any plugins that work well. Any recommendations/alternatives would really help me out. I figured there must be some other tools by now, and if anyone knows it has to be this sub. Basically, I need a tool that can provide info on a wide range of subjects, not limited to just one field. The information provided by the tool should be accurate and from credible sources. Thank you all. submitted by /u/AccidentallyRotten [link] [comments]  ( 9 min )

"If AGI goes bad, can't we just turn it off?" Personally I feel the best way to address this common talking point is with an analogy. Spiders think they could stop all humans if they just withheld all the webs and web making material from us. Without those tools, humans couldn't catch flies and surely they'd starve to death? Spiders can't fathom the range of alternate methods for procuring food and thriving. Within even a single hour of runtime, a super AGI will likely have diversified away from the human electrical grid in ways we couldn't even imagine. The counter argument is, that it would take time to build these pieces together. It after all took us 100 years to get to where we are with the grid. The counter-counter argument however is the AGI doesn't ned to, it can 5D chess us so that all our future actions will fulfil that goal with some slight nudging here and there. Fascinating stuff - ultimately though, i'm in the camp of AGI won't happen over night like Frankenstein via a flip of a switch. As AI evolves so do we, gains are incremental with the occasional blips; so whilst this is super fun to talk about, I think the case of us getting blindsided is unlikely. I could be wrong...and I probably am. submitted by /u/kippersniffer [link] [comments]  ( 9 min )

submitted by /u/Philipp [link] [comments]  ( 8 min )

if you haven't heard already the Taliban are killing thousands of ethnic Shia in Afghanistan. Every single LLM I Tried couldn't answer basic questions on the Talibans gdp vs how organized an actual genocide would look like with the military , police and others parts of the government. I Think where already aware almost all these tech giants work with countries like China (atleast bard from Google which has worked with north korea and china is admitting their is a genocide) other countries that commit genocide like them. And other models made by people on hugging face which are uncensored even with my 3060ti barely run on my pc. We need an actual uncensored cloud model ffs submitted by /u/loizo78 [link] [comments]  ( 8 min )

submitted by /u/Choochy89 [link] [comments]  ( 8 min )

I am trying to train a DQN to play chess using a combination of Tianshou and PettingZoo. However, for a reason I cannot locate, after anwhere from 15-25 passes through the forward function, the size of the batches starts decreasing, until it falls all the way to 1, before throwing a warning that n_step isn't a multiple of the number of environments, jumping to a size = the number of training environments and then the training agent's batch size before erroring out. My best guess is that somehow truncated games aren't being properly added to the batch, but that doesn't quite explain why each subsequent batch is equal or smaller in size. I am at a loss for how to debug this. Everything is in this Python Notebook. submitted by /u/lcmaier [link] [comments]  ( 9 min )

I'm currently working on a project for Imitation Learning from multiple perspectives. The base Imitation Learning algorithm I'm currently using is GAIL. Working with GAIL has been very frustrating because it's incredibly seed dependent and unstable. This makes progress and iteration speed for experiments/modifications built on top of it very slow. As I'm not an expert in Imitation Learning: Does anybody with experience know more stable alternatives (or improvements) to GAIL? The setting I'm considering is Learning from Observations (LfO), so I don't think that DAgger will work. I've done some preliminary search and found this method https://arxiv.org/pdf/2004.04650.pdf. However, the authors don't compare it to GAIL. Thanks in advance for any suggestions! submitted by /u/timo_kk [link] [comments]  ( 9 min )

Hi all: I am trying to train a competitive scenario in a Multiagent particle environment( I am now using the Pettingzoo API). The Algorithm I am now using only support discrete action space. But I want to evaluate agents with one side's policy keep fixed and let the other side's policies be the trained policy. The policy can be simple( like if the target for one side agent is to chase the other side, their policy is directly following the trajectory for their target). The core.py for the petting zoo, it has # return all agents controllable by external policies @property def policy_agents(self): return [agent for agent in self.agents if agent.action_callback is None] # return all agents controlled by world scripts @property def scripted_agents(self): return [agent for agent in self.agents if agent.action_callback is not None] But in the step for the environment, it seems the environment directly controls the policy agent. My understanding is scripted agent is RL policy output and the Policy agent can be controlled by other policies. My question is : if my policy output is the desired position for each timestep, but now the MPE's control dynamic is learned the acceleration's increment, and it's discrete, how can I implement the policy as one side of my competitive case? if I can control the policy agent base on policy_agent, how can I step both policy and script agent in env? if I can control the agent separately, like my RL output can be discrete but the policy output can be continuous position. how to define the termination or truncation for all agents? submitted by /u/Gloria_1126 [link] [comments]  ( 9 min )

I am trying to train the cartpole environment using policy gradients algorithm. I want to train using the GradientTape method of tensorflow. I have been trying for a long time, but still it hasn't converged. What am I doing wrong? ​ import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import keras.backend as K import matplotlib.pyplot as plt class PolicyGradientModel(keras.Model): def __init__(self, num_actions): super().__init__() self.hidden1 = layers.Dense(24, activation='relu') self.hidden2 = layers.Dense(120, activation='relu') self.out = layers.Dense(num_actions, activation='softmax') def call(self, inputs): x = self.hidden1(inputs) x = self.hidden2(x) return self.out(x) def action_prob(self, state): prob = self.predict(np.ex…  ( 9 min )

Here is my vectorized PPO implementation, that I wrote (with a lot of help from this community). These are my results on the Acrobot-v1 environment. The way I computed the reward for my vectorized implementation was that I added all the rewards across all environments. An ideal Acrobot agent should receive a reward of 0. Please let me know if I am missing any information or if any clarification is required. I skipped a part, which was suggested by the community a few months ago - updating the gradients using minibatches. The reason I skipped it is that, I don't understand how this works and anyway Acrobot should be an easy environment to learn. https://preview.redd.it/0vs9ur585mfb1.png?width=622&format=png&auto=webp&s=ebc007a9f797bd0b97b805d010dbd097c0be8906 Also, I keep getting this error at the end of my code. But I haven't bothered fixing it as it doesn't seem to affect my algorithm - Exception ignored in: Traceback (most recent call last): File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\vector_env.py", line 139, in __del__ File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\vector_env.py", line 121, in close File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\gym\vector\async_vector_env.py", line 327, in close_extras AttributeError: 'NoneType' object has no attribute 'TimeoutError' ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hi guys this is my first post. I am building my first machine learning model to predict costs of various projects by month. Each row can be identified with a column project name and month (these two are dropped for testing). The rest of the columns are various features that can help predicting the end project cost. I want to be able to predict costs on a monthly basis. My question is how should I split the data because each row is a unique project and month. Is it ok to just do a train test split and have earlier project months be in the testing set while having future project months be in the training set? Isn’t that giving the model too much information? Or should I train on each project’s indices and leave one project as testing for each project I have? I’m worried about overfitting with that one. Thanks in advance for any help! submitted by /u/Single_Swing_3173 [link] [comments]  ( 9 min )

Hi guys this is my first post. I am building my first machine learning model to predict costs of various projects by month. Each row can be identified with a column project name and month (these two are dropped for testing). The rest of the columns are various features that can help predicting the end project cost. I want to be able to predict costs on a monthly basis. My question is how should I split the data because each row is a unique project and month. Is it ok to just do a train test split and have earlier project months be in the testing set while having future project months be in the training set? Isn’t that giving the model too much information? Or should I train on each project’s indices and leave one project as testing for each project I have? I’m worried about overfitting with that one. Thanks in advance for any help! submitted by /u/Single_Swing_3173 [link] [comments]  ( 9 min )

I am trying to implement REINFORCE (Monte Carlo Policy Gradient) on flappy bird (flappy-bird-gymnasium) and I am unable to make the ai cross even just 1 pipe. I am experiencing a constant avg score throughout all episodes from start to end and no change in policy loss as well (sometimes). I tried a lot of different hyperparameter combinations as well. I have checked the policy (neural network) and the algorithm code multiple times and they seem to be fine. I am just not able to determine why the AI isn't learning or is able to cross even a single pipe. If someone can help me out, it would be really helpful! code - https://github.com/Sookeyy-12/REINFORCE_Projects there's also a video of the agent's gameplay in this repo. submitted by /u/Sookeyy [link] [comments]  ( 9 min )

The paper reviews for AACL 2023 are out, feel free to share your thoughts and feelings! How did you do? submitted by /u/Pomhelpme [link] [comments]  ( 8 min )

Hi, same as other folks, I was quite curious about the recent GZIP paper presented at ACL 2023, where the authors demonstrate strong text classification performance by using a compression-based distance function in a KNN model. However, in the end, I am not sure whether GZIP can fully live up to the hype. I tested a very simple bag-of-words distance and found that it can achieve better results compared with GZIP, while being also faster. In a nutshell, I think we can say that: Yes, KNN (with some sensible distance function) is an interesting approach, particularly for few-shot/low-resource scenarios. No, GZIP (even though it's a cool idea) is not a very sensible distance function. Simply using a bag-of-words achieves better results, and is much faster. Here's my full write-up: https://arxiv.org/abs/2307.15002 [PS: A short comment on the GZIP evaluation issue that has been widely discussed. Indeed, as was also shown in a popular blogpost, the displayed accuracy of GZIP in the original paper is optimistic. Therefore, I show correct/realistic accuracy numbers for all methods that I tested. However, the main point of my note is not to make a SOTA comparison or something, but rather just provide a reminder that bag-of-word is a good method for starters and a strong baseline, and can perform better than more complex GZIP for KNN classification] submitted by /u/juopitz [link] [comments]  ( 9 min )

Hi guys, I built something that you might enjoy. Totally free and open source. Basically it lets you create text that you can prove came from you. For example, in my colab demo: https://colab.research.google.com/drive/1764iRR-EFJl43KIKhrb2H0CTcT0b1vQm?authuser=2#scrollTo=qyKud8qtM3vA I prove that I generated the text: 'The world is constantly changing due to technological advancements, which include the creation of powerful language models and advanced robotics technologies. A Computer Science degree can help one be involved in these changes and apply their knowledge to everyday life, as practical applications of technology.' The text is a bit wonky as the generation model is just a small paraphrasing fine-tuned model I pulled off Hugging Face, but it's pretty natural even at this earl…  ( 9 min )

Hey everyone! I've got a rather large dataset of images, mostly featuring humans in a variety of poses (think along the lines of a collection of people practicing yoga and the like). My goal is to cluster these images based on the poses, so I can avoid the tedious task of manually sifting through each one to find all the people doing handstands, splits, and so forth. My initial thought was to run OpenPose on all these images, then perform clustering based on the output from OpenPose. Does this sound like a feasible approach? Do any of you have better suggestions? Or perhaps there's already an existing software solution that can do this? Thanks! submitted by /u/cyan2k [link] [comments]  ( 9 min )

Read the release notes: https://github.com/kornia/kornia/releases/tag/v0.7.0 -------------------- Image API In this release we have added a new Image API as placeholder to support a more generic multibackend api. You can export/import from files, numpy and dlapck. https://preview.redd.it/0d5tvjxmeofb1.png?width=621&format=png&auto=webp&s=9af05a037770132c9a267b68dcd9ab8182557517 Object Detection API We have added the ObjectDetector that includes by default the RT-DETR model. The detection pipeline is fully configurable by supplying a pre-processor, a model, and a post-processor. Example usage is shown below https://preview.redd.it/rtbayqpneofb1.png?width=680&format=png&auto=webp&s=4d46edeeee4027e08a493cb15182ea0ddc42bc5d https://preview.redd.it/ukcg9enoeofb1.png?width=680&format=png&a…  ( 9 min )

I have been testing OpenFace with some telescope lenses (focal length 8-16mm) to test the performance of the pose estimation at mid range (2-4 meters). I have been passing the camera and lens intrinsics to OpenFace but have been finding that the pose estimation has not been great. Does anyone with more ML experience know at what point in the OpenFace pipeline the issues could be coming from? e.g. the point distribution model or the training data submitted by /u/DoPe-_-SoaP [link] [comments]  ( 8 min )

Hi, this is my first time posting here. My goal is to check if optical flow can improve a pretrained model's performance. The pretrained model: gives an output as a binary mask for the object its trying to detect. The optical flow: is the motion of pixels between frames, this model also gives an image shaped flow vector. I want to combine the mask by pretrained model and optical flow information and send it to another model to improve its performance. For the model: I can use U-net or a simple convolution encoder-decoder model, but I am confused about which will be the best model architecture for it. ​ submitted by /u/luxuryBubbleGum [link] [comments]  ( 9 min )

I am trying to develop some app ideas based on LLM (i.e., summarize and extract entities from articles), but I can't afford any paid API access right now (including OpenAI), are there free alternatives to it? submitted by /u/Guyserbun007 [link] [comments]  ( 8 min )

Hi, ​ I want to use a new data representation instead of float for fine-tuning/testing a model (e.g., DNN) in Pytorch. The basic operations (add/sub/multiply/division) in my data type is different from floating point. My question is if it is possible to implement these operations (+,-,*,/) and force all of functions in Pytorch (e.g., torch.add(), torch.sum(), torch.nn.Linear(), conv2d, etc.) to use my basic arithmetic implementation? If so, could you please guide me how can I do it? Because I think otherwise it takes so much time and effort; first, I have to find which functions my model calls (which I dont know how to do it) and, then, I have to replace them one by one. This becomes complicated for a large model. I found this link from Pytorch that shows how to extend pytorch. But it seems that it is not comprehensive enough to answer my question. ​ Thank you very much! submitted by /u/Impossible-Froyo3412 [link] [comments]  ( 9 min )

The convergence of Oracle Cloud Infrastructure (OCI) and Hitachi Application Reliability Centers (HARC) to magnify outcomes for customers. Tech giants Oracle and Hitachi Vantara are marching together to magnify cloud outcomes. Join us for the Oracle and Hitachi Vantara virtual event, where we discuss how businesses can get the most out of OCI and HARC.… Read More »DSC Webinar Series: OCI & HARC: Modernizing Workloads in the Oracle Cloud The post DSC Webinar Series: OCI & HARC: Modernizing Workloads in the Oracle Cloud appeared first on Data Science Central.  ( 18 min )

Artificial intelligence, or AI, has often been depicted as a terrifying force, from HAL 9000’s chilling declaration in “2001: A Space Odyssey” to the apocalyptic machine uprising in the Terminator movies. However, in reality, AI has become an integral part of our daily lives, with AI-powered Android devices in our pockets. Though we may not… Read More »Emerging AI statistics and trends to watch The post Emerging AI statistics and trends to watch appeared first on Data Science Central.  ( 20 min )

Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized experiences for audiences and improves the overall quality of the final products. One significant benefit of generative AI is creating unique and personalized experiences for users. For example, […]  ( 14 min )

SageMaker Distribution is a pre-built Docker image containing many popular packages for machine learning (ML), data science, and data visualization. This includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab. In addition to this, SageMaker Distribution supports conda, micromamba, and pip as Python […]  ( 6 min )

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document […]  ( 13 min )

In this edition: A new anonymous token protocol balances fraud detection and privacy; survival instinct in offline RL; Nimble offers rollback protection for confidential cloud services; improved machine learning force fields for molecular dynamics. The post Research Focus: Week of July 31, 2023 appeared first on Microsoft Research.  ( 11 min )

submitted by /u/No-Platypus4021 [link] [comments]  ( 8 min )

Hello all, ​ I am delving into the exciting realm of GenAI and LLMs. I have a few questions I hope you can help me with: ​ When should I opt for supervised fine-tuning rather than prompt engineering with retrieval? What are the associated costs of supervised fine-tuning? How many high-quality observations are typically required for successful supervised fine-tuning? What are the frameworks and computional requirements usually involved in supervised fine-tuning, and how can I implement them in code? any tutorials available? Can the model adapt and learn new jargon or specific tasks that might not be extensively covered during the pre-training phase? ​ I understand that a combination of supervised fine-tuning and reinforcement learning, with human feedback through a reward model, is considered the best approach. However, given that the latter method can be costly and falls under the domain of heavy research, it is probably less feasible for medium-sized organizations. submitted by /u/quilograma [link] [comments]  ( 9 min )

I went down this rabbit hole of trying to understand when domain mapping approaches like stargan or mind the gap succeed and fail. For example, it should be easy to map males (source domain) with large eyes and brown hair onto females (target domain) with analogous eye and hair color. It should be relatively harder to map different car models onto images taken of one German Shepard dog at different ages. this makes intuitive sense and the terms “domain misalignment “ and “large domain shift“ come to mind, but i cannot find an in-depth discussion of this topic. Any thoughts? submitted by /u/Rotfisch [link] [comments]  ( 9 min )

NeurIPS 2023 paper reviews are visible on OpenReview. See this tweet. I thought to create a discussion thread for us to discuss any issue/complain/celebration or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get a low score because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the score is. submitted by /u/zy415 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2307.16789 Github: https://github.com/OpenBMB/ToolBench Abstract: Despite the advancements of open-source large language models (LLMs) and their variants, e.g., LLaMA and Vicuna, they remain significantly limited in performing higher-level tasks, such as following human instructions to use external tools (APIs). This is because current instruction tuning largely focuses on basic language tasks instead of the tool-use domain. This is in contrast to state-of-the-art (SOTA) LLMs, e.g., ChatGPT, which have demonstrated excellent tool-use capabilities but are unfortunately closed source. To facilitate tool-use capabilities within open-source LLMs, we introduce ToolLLM, a general tool-use framework of data construction, model training and evaluation. We first …  ( 9 min )

Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. FFTs are used by many algorithms, not only for signal processing. For example, you can efficiently calculate convolutions with them, which has applications in CNNs and feature generation. I used to post on the latest features implemented in the codebase and there has been a major update released today. It brings: -Major library design change - from single header to multiple header approach, which improves structure and maintainability. Now instead of copying a single file, the user has to copy the vkFFT folder contents. -VkFFT has been rewritten to follow the multiple-level platform structure, described in the VkFFT whitepaper. All algorithms have been split into res…  ( 9 min )

https://github.com/dora-rs/dora submitted by /u/haixuanxaviertao [link] [comments]  ( 8 min )

Hey everyone, As many of you probably know Reinforcement Learning from Human Feedback (RLHF) was the core technique used to produce ChatGPT and similar AI assistants that followed. RLHF replaces human feedback in an RL schema with a preference model that is trained according to a dataset of human preferences. Anthropic has devised an extension of this idea in which an AI model (rather than humans) is used to generate the data which ultimately trains the preference model. This method, called Reinforcement Learning from AI Feedback uses a "constitution" to guide the feedback model in terms of what outputs are preferable to others. I go over the research in How Reinforcement Learning from AI Feedback Works. In short, the authors find that they are able to train a non-evasive harmless agent using a short constitution. The method is found to be superior to RLHF, and constitutes a Pareto improvement over RLHF models. https://preview.redd.it/qaivl8f1ljfb1.png?width=1179&format=png&auto=webp&s=a0941f2ce0ccdcf0557cf19b7f4b48fa712a66f2 Let me know what you think, I'm happy to answer any questions! submitted by /u/SleekEagle [link] [comments]  ( 9 min )

Hi there, First off, apologies if this kind of post isn't allowed. I tried messaging the mods in advance, but didn't get a reply. Of course feel free to delete if it's not. I'm an academic at the University of Cambridge's Computer Lab, and I'm looking to get some insights from people that work with algorithmic systems (e.g. ML systems) in a professional capacity. The aim of the research is to document some of the approaches, attitudes, and challenges associated with record-keeping for these types of systems, and write them up for an academic conference. If you're a professional working with algorithmic/ML systems, and happen to have a spare ~20 minutes, would you mind answering some questions? The link to the questionnaire is here: https://cambridge.eu.qualtrics.com/jfe/form/SV_3n6RuowNogZKG34 Thanks very much! I'd be more than happy to come back and share the results/paper here if that's of interest to people? submitted by /u/cnorval [link] [comments]  ( 9 min )

The official python package for the "'Low-Resource' Text Classification: A Parameter-Free Classification Method with Compressors" has now been released on pypi: npc-gzip v0.1.0 Abstract: Deep neural networks (DNNs) are often used for text classification due to their high accuracy. However, DNNs can be computationally intensive, requiring millions of parameters and large amounts of labeled data, which can make them expensive to use, to optimize, and to transfer to out-of-distribution (OOD) cases in practice. In this paper, we propose a non-parametric alternative to DNNs that’s easy, lightweight, and universal in text classification: a combination of a simple compressor like gzip with a k-nearest-neighbor classifier. Without any training parameters, our method achieves results that are competitive with non-pretrained deep learning methods on six in-distribution datasets.It even outperforms BERT on all five OOD datasets, including four low-resource languages. Our method also excels in the few-shot setting, where labeled data are too scarce to train DNNs effectively. This paper has made some waves on this subreddit and in the community in general over the last 2 weeks. We've seen the bugs around training/testing data leakages and varying claims in accuracy. Our hope with this package is to get the code into everyone's hands first to solve whatever use case you currently have for this technology and second to make the code more readily available for additional community testing. Links: * https://pypi.org/project/npc-gzip/ * https://github.com/bazingagin/npc_gzip * https://aclanthology.org/2023.findings-acl.426/ submitted by /u/dfcHeadChair [link] [comments]  ( 9 min )

submitted by /u/Jean-Porte [link] [comments]  ( 8 min )

This is one of the ICML 2023 papers I focused in on in a sea of LLM stuff. Trying to figure out simple ways to implement this and adapt it to regression problems. Thoughts? submitted by /u/quantthrowaway69 [link] [comments]  ( 8 min )

I was wondering if there's already something like CLIP (the model that looks at an image and describes it), but for videos. So you show a video of, say, a dog jumping and grabbing a tennis ball and it outputs "dog grabbing a tennis ball", something like that. My first thought was object detection, and input that interaction of the objects (tennis ball, dog) to the model with the target being "dog grabbing tennis ball". My ultimate goal being real-time description for, say, sports casting. I'm sure something like this is what cars use to drive themselves, or not? Any info is appreciated! submitted by /u/Yip37 [link] [comments]  ( 9 min )

Can someone please help me with this question? Please let me know if any clarification is needed. Thanks so much! submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

I'm currently working on a research project that involves using deep reinforcement learning with drones. I'm looking for recommendations on drones that would be suitable for this type of research. I am looking for something of the shelf. submitted by /u/anointedninja [link] [comments]  ( 8 min )

So i want to make a bot that can play a game with only the visual data and no other fancy stuff. I did manage to get all the data i need (i hope) using a code that uses open-cv to get data in real time Example:Player: ['Green', 439.9180603027344, 461.7232666015625, 13.700743675231934] Enemy Data {0: [473.99951171875, 420.5301513671875, 'Green', 20.159990310668945]} Box: {0: [720, 605, 'Green_box'], 1: [957, 311, 'Green_box'], 2: [432, 268, 'Red_box'], 3: [1004, 399, 'Blue_box']} can anyone suggest a way to make one. Rules: - You can only move in the direction of mouse. -You can dash in direction of mouse by LMB. -You can collect boxes to get HP and change colors. -Red color kills Blue kills Green Kills Red. -There is a fixed screen. -You lose 25% of total HP when you dash. -You lose 50% of HP when you bump into players (of color that kills or there HP is > than you. ​ Visualization of Data. submitted by /u/SIJ_Gamer [link] [comments]  ( 9 min )

Announcements Top Stories In-Depth The post DSC Weekly 1 August 2023 appeared first on Data Science Central.  ( 19 min )

Generative AI has been around for a long time. Some sources say that it appeared as early as the 1950’s. Other sources point to the first rudimentary chatbots that were introduced in the 1960’s. Whatever the true point of origin, we can all agree that those were small pebbles on the historical timeline compared to… Read More »I bet you think this article is about ChatGPT The post I bet you think this article is about ChatGPT appeared first on Data Science Central.  ( 22 min )

If I could name one reason why business will face at least one more AI winter, it’s the lack of nuance in most business AI discussions. The buzz about large language models (LLMs) has sucked much of the oxygen out of the air for complementary technologies. The truth is that LLMs are no more a… Read More »Data tribalism and the AI nuance deficit The post Data tribalism and the AI nuance deficit appeared first on Data Science Central.  ( 20 min )

The post DSC Webinar Series: Influence Data-Driven Decisions Based On Your Communication Style appeared first on Data Science Central.  ( 17 min )

There are thousands of articles explaining the differences between data scientist and machine learning engineer. Data science gets broken down even further, with data analysts contrasted to researchers. Professionals skilled in all these domains are called unicorns and believed not to exist. Indeed, they may not work for companies, and ignored when applying for a… Read More »The Rise of the Dual Data Scientist / Machine Learning Engineer The post The Rise of the Dual Data Scientist / Machine Learning Engineer appeared first on Data Science Central.  ( 21 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

DoNotPay, an AI lawyer bot known as ChatGPT4, is transforming how users handle legal issues and save money. In under two years, this innovative robot has successfully overturned more than 160,000 parking tickets in cities like New York and London. Since its launch, it has resolved a total of 2 million related cases.[1] Microsoft hints Windows 11 Copilot with third-party AI plugins is almost here.[2] In an analyst note on Tuesday, the financial services arm of Swiss banking giant UBS raised its guidance for long-term AI end-demand forecast from 20% compound annual growth rate (CAGR) from 2020 to 2025 to 61% CAGR between 2022 to 2027.[3] The next generation of the successful OpenAI language model is already on the way. It has been discovered that the North American company has filed a registration application for the GPT-5 mark with the United States Patent and Trademark Office.[4] Sources: [1] https://citylife.capetown/uncategorized/donotpay-ai-bot-saves-users-money-by-overturning-parking-tickets-and-more/302279/ [2] https://www.itvoice.in/microsoft-hints-windows-11-copilot-with-third-party-ai-plugins-is-almost-here [3] https://venturebeat.com/ai/ubs-projects-61-compound-annual-growth-rate-for-ai-between-2022-and-2027/ [4] https://www.gearrice.com/update/openai-confirms-gpt-5-and-gives-us-the-first-clues-about-it/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/Jane-in-the-jungle [link] [comments]  ( 8 min )

Other features might include: - searching the web for the same or similar image - basing the chat prompt off the image submitted by /u/Maelasae [link] [comments]  ( 8 min )

i wanted to ask the AI experts for any tattoo ideas, anything like a symbol or word, something unique that represents AI, i was thinking of a CPU but thats a bit meh and not really a symbol, let me know :) submitted by /u/Equivalent-You5810 [link] [comments]  ( 8 min )

As a web developer, I was constantly tired of switching between tabs just to translate a word or two, or to get a quick answer to a burning question from AI. The constant back-and-forth was draining my time and energy. So, I took matters into my own hands and developed a Chrome extension that allows you to get an answer from AI without ever leaving the comfort of your current tab, and specifically - the comfort of your current text field. It may seem like a simple solution, but trust me - it's a game-changer when trying to save time and energy. Assuming that there's a chance some of you might be experiencing the same frustration, I'd like to share this tool with you. For anyone thinking: "Wait, but there are already tools that let you use AI inside the current browser tab" - yeah, there are. BUT can they scrape website data from a simple URL in order to get context for the response? Can other tools read PDFs? Do these tools let you control every setting to the smallest detail? Probably not. Well this tool does let you do all that. You can find it on Chrome store as "Wou AI" Let me know how it works out for you, and I would greatly appreciate any feedback or suggestions for future functions. submitted by /u/MantasDigital [link] [comments]  ( 9 min )

submitted by /u/simsirisic [link] [comments]  ( 8 min )

I'm looking for reviewers for my book, "AI Self Portraits" which is coming out on Amazon on the 21st. I might even put your quote on the back cover! ​ https://preview.redd.it/cqmp1ggllhfb1.png?width=1024&format=png&auto=webp&s=cc7c087f7c2be103b53f2014acd991c947e6cb7f ​ submitted by /u/KarneyHatch [link] [comments]  ( 8 min )

After analyzing 15,000 samples in the dataset, we noticed that increasing the number of images doesn't significantly improve the scoreboard recognition quality for our neural network. However, what's more interesting is how the network performs in different formats. When deployed in TFJS format on a website, it often behaves strangely, detecting objects where there are none. On the other hand, in TFLite format, such failures are almost non-existent. https://preview.redd.it/fedfa8lzchfb1.jpg?width=700&format=pjpg&auto=webp&s=850526791a75465e267afbed6ac1bc119b9ae6ae If you access the link on your mobile phone and grant camera permission, you'll witness the neural network (in TFJS format) attempting to find objects even when there are none. ​ submitted by /u/moseich [link] [comments]  ( 8 min )

I was Wondering If There is an AI Software, Smart enough That Can Give Excellent Quality Transcript if i give the link of a youtube video. Basically the Feature i am Looking For Should be The Ability to Detect The Narrator And Speaker By Names ( Not SPeaker 1, 2 etc ). Would really appreciate your help as my own search has led me to a dead-end. submitted by /u/Richie_Boy_ [link] [comments]  ( 8 min )

How do channels like TriggerHappy Productions and WeskerandFriends get the A.I. voices of all these Resident Evil characters? submitted by /u/Conscious-Theory-850 [link] [comments]  ( 8 min )

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. This wealth of information, while essential for patient care, can also be overwhelming and time-consuming for medical professionals to sift through and analyze. Efficiently summarizing and extracting […]  ( 13 min )

Advertising agencies can use generative AI and text-to-image foundation models to create innovative ad creatives and content. In this post, we demonstrate how you can generate new images from existing base images using Amazon SageMaker, a fully managed service to build, train, and deploy ML models for at scale. With this solution, businesses large and […]  ( 8 min )

Principal NVIDIA artist and 3D expert Michael Johnson creates highly detailed art that’s both technically impressive and emotionally resonant.  ( 6 min )

NVIDIA joined Pixar, Adobe, Apple and Autodesk today to found the Alliance for OpenUSD, a major leap toward unlocking the next era of 3D graphics, design and simulation. The group will standardize and extend OpenUSD, the open-source Universal Scene Description framework that’s the foundation of interoperable 3D applications and projects ranging from visual effects to Read article >  ( 6 min )

After analyzing 15,000 samples in the dataset, we noticed that increasing the number of images doesn't significantly improve the scoreboard recognition quality for our neural network. However, what's more interesting is how the network performs in different formats. When deployed in TFJS format on a website , it often behaves strangely, detecting objects where there are none. On the other hand, in TFLite format, such failures are almost non-existent. If you access the link on your mobile phone and grant camera permission, you'll witness the neural network (in TFJS format) attempting to find objects even when there are none. https://preview.redd.it/gosobymachfb1.jpg?width=585&format=pjpg&auto=webp&s=e7cb8e8e3ff49e39715009c4940d9769a1db39ab submitted by /u/moseich [link] [comments]  ( 8 min )

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An up-down permutation of an ordered set is a permutation such that as you move from left to right the permutation alternates up and down. For example 1, 5, 3, 4, 2 is an up-down permutation of 1, 2, 3, 4, 5 because 1 3 2. Up-down permutations are […] Up-down permutations first appeared on John D. Cook.  ( 5 min )

Suppose you have data that for some reason has been summarized into bins of width h. You don’t have the original data, only the number of counts in each bin. You can’t exactly find the sample mean or sample variance of the data because you don’t actually have the data. But what’s the best you […] Variance of binned data first appeared on John D. Cook.  ( 5 min )

A very crude way to estimate π would be to find the perimeter of squares inside and outside a unit circle. The outside square has sides of length 2, so 2π < 8. The inside square has sides of length 2/√2, so 8/√2 < 2π. This tells us π is between 2.82 and 4. Not […] Ancient estimate of π and modern numerical analysis first appeared on John D. Cook.  ( 6 min )

Hi all, Curious if anyone has recommendations on models to use to interpret the data in tables? I'm playing around with Google's Matcha model, which performs fine. seems like extracting the data out of a table and asking GPT4 to analyze it performs a bit better but requires extra steps. I'm specifically not looking to interpret graphs, but rather tables. e.g., can i ask the model to identify if there are any errors in the table / any data points that don't tie if the rows are supposed to sum up. submitted by /u/eyeronthrone [link] [comments]  ( 8 min )

I'll likely be downvoted to hell but here goes: Prices for The AI Conference double at midnight Pacific. 46 Speakers, 10+ topics, 2 Days plus a hackathon at night! Join us to learn and collaborate with scientists, engineers and founders from the top AI companies and projects. Speakers include: Ben Mann | Co-Founder | Anthropic Peter Norvig | Director of Research | Google Nazneen Rajani | Research Lead | Hugging Face Igor Markov | Research Scientist | Meta Bryan Catanzaro | VP Of Research | Nvidia Ram Sriharsha | VP of Engineering and R&D | Pinecone Jerry Liu | Co-founder | LlamaIndex Harrison Chase | Co-founder | LangChain Alex Chao | Product Manager Semantic Kernel | Microsoft See All Speakers Last chance to get in on early bird pricing (save $400 on a 2 day pass). If you can read this and I'm not downvoted to hell, use discount code redditlove for 25% off. Use discount code "student" for $200 student tickets \*Must Use EDU email to register* **This is my event and therefore self-promotion ​ ​ submitted by /u/shonburton [link] [comments]  ( 9 min )

For the past several years this subreddit has been my favorite source to keep up with new, interesting ideas and research from all over the field. It's great to have a way to break out of my own insular research bubble and spread out a bit more. Unfortunately, it looks like that era has passed. The sub has been seemingly shifting away from research in the past 1-2 years. Whenever research is posted, it is almost always LLM based with very little variety (considering the plethora of research areas in ML). I don't mean to assert that this is a bad thing, as the constant upvotes indicate that there is a high demand for LLM projects and research. Heck, I'm also interested in lots of the recent work with LLMs, and I plan to keep up with it – but I also would also love a venue with a diversity of ideas and topics. Machine learning is a HUGE field, and only focusing on a small subset of it seems like a waste. I don't mean to rant, but rather to ask: are there any other subreddits like this, or perhaps, any other active communities with a broader scope? Or if this doesn't exist, is there a demand for it? Or is it just me? submitted by /u/ejmejm1 [link] [comments]  ( 9 min )

Is there any documentation available which will help in implementing elasticsearch HNSW ANN search in python? I've searched a lot but i cant find anything in official documentation too Any help will be appreciated. TIA submitted by /u/adiraat [link] [comments]  ( 8 min )

Everyone always seems to use CUDA 11.7. Is there a reason for this? What is the factor that limits the CUDA version used? Are there any speed/efficiency advantages to using a more recent version of CUDA, such as CUDA 12.0? What exactly is the limiting factor here, PyTorch? I've looked in the PyTorch docs but I don't see where the CUDA version is defined. Where can I find the maximum CUDA version I can use with the latest (or any given) PyTorch version? Thanks! submitted by /u/Pan000 [link] [comments]  ( 8 min )

Hey guys, I am working on a horse racing model to identify the probabilities of each horse winning a race. I currently have a feed forward NN with a final SOFTMAX layer to simulate probabilities of each horse winning using cross-entropy loss. My plan here being that if the model outputs, [0.05, 0.4, 0.2, 0.15, 0.2] then horses 1-5 have the corresponding probability of winning. The model has been trained like a regular classification task where the target is a one-hot vector describing the winner. Unlike previous work I have done where SOFTMAX output lends itself to some "confidence" score, this task requires that the model outputs be indicative of probabilities. My concern is that experientially, NNs tend to be overconfident with their answers in this type of setting. However, I wish to keep using a NN as each race datapoint has around 3k features - did not find good results with XGBoost. Any good practices for modelling probabilities in this sort of scenario? For context, the probability of a horse winning is what sets the odds for that horse. submitted by /u/HStuart18 [link] [comments]  ( 9 min )

I was wondering if anyone knows how difficult it is to set up a server to run the 70B llama / llama 2 variants like these top ones on the hugging face leaderboard https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard What type of gpu would I need to set it up? Would the high ram t4 you get with Google colab+ be enough or does it require more power / space? Thanks in advance! submitted by /u/Additional_Elk4745 [link] [comments]  ( 8 min )

Article available here: https://arxiv.org/pdf/2307.09084.pdf Thoughts? submitted by /u/MuffinB0y [link] [comments]  ( 8 min )

Hi. I've shipped an update to my rclip – a command-line photo search tool powered by CLIP. Now, you can add and subtract image and text queries from each other; here are a few usage examples: cd photos && rclip horse + stripes cd photos && rclip apple - fruit cd photos && rclip "./new york city.jpg" + night cd photos && rclip "2:golden retriever" + "./swimming pool.jpg" cd photos && rclip "./racing car.jpg" - "2:sports car" + "2:snow" If you want to see how these queries perform when executed on the 1.28 million images ImageNet-1k dataset, check out the demo on YouTube: https://www.youtube.com/watch?v=MsTgYdOpgcQ. rclip source code is published on GitHub under the MIT license and offers a pre-build distributable for Linux (installation instructions are in the README): https://github.com/yurijmikhalevich/rclip. Give it a try and let me know what you think! submitted by /u/39dotyt [link] [comments]  ( 9 min )

Im trying to research into open source text models [like Llama] and image models [like Stable Diffusion]. My goal is to give the model(s) a picture of birds and bees, then ask it to "circle" the bees. The idea is, when given an image, it would produce coordinates on that image where the line should be circled. It could also represent where it should "click" on all the bees. Does something like this exist? submitted by /u/MindWithEase [link] [comments]  ( 8 min )

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submitted by /u/williamsweep [link] [comments]  ( 8 min )

Greetings! I'm going through the initial equations that define most of the theoretical framework for the specialization. One curious thing I noticed with equations 3.5 and 3.6 is that they use the conditional distribution p(s′,r∣s,a) without including any priors. I'm talking about priors because, unless I'm missing something huge, the definition of the expected value for the reward (for both 3.5 and 3.6) should use the joint distribution for all 4 dimensions (next state, reward, current state, action). From that joint distribution, we can factorize it to show p(s′,r∣s,a). For example, one factorization that seems to make sense for this kind of model is p(s′,r,s,a) = p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) which would turn, for example, equation 3.5 into r(s,a) = ∑​ ∑ ​r ⋅ p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) (Note: the two sums are for "r" and "s' ". I wrote like that because I don't know write it in Latex or similar...) What am I missing? Is it because s and a are given as parameters of the function r(s,a) meaning that p(s) = p(a) = 1? If the factorization above is the right one for those equations, is this the only factorization used in the entire book? Thanks in advance! submitted by /u/SupBiebi [link] [comments]  ( 9 min )

So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )

Are there big action space stochastic games that are implemented in OpenSpiel or equivalent? I played around Markov soccer game a lot but it's solvable with tabular methods and I was looking for games with at least more than 500 actions both players can take as a testbed for more complicated action spaces? submitted by /u/Potential_Biscotti14 [link] [comments]  ( 8 min )

Hello everyone! I'm trying to use reinforcement learning to solve a problem in the power market. The problem is about finding the best strategy for bidding on electricity for each hour of the day, considering both buying and selling options. Let's say we have a generator that can produce up to 800MW of electricity per day, and it can be charged up to 200MW per hour. After charging it for 4 hours continuously, it reaches its maximum capacity, and we can't charge more until we discharge some electricity. We have access to data from the past 5 years, including information about temperature, hydro, gas prices, and locational marginal price, which is important for determining profit. For instance, if we buy 10MW of electricity for a specific hour, our profit for that hour is 10 times the locational marginal price. The goal is to maximize profit at the end of the day while making sure that the total electricity bought and sold is equal for all days. This means we want to avoid wasting electricity. I initially tried using deep Q-learning, where the agent's state consists of data from the past 3 days, and the agent can take actions to buy or sell a certain amount of electricity for one hour. However, this approach doesn't seem to provide accurate results, and it works step by step, not considering the overall outcome for the whole day. So, I'm looking for help on how to build an agent capable of producing 24 bids for 24 hours, considering the constraints of the generator's capacity and ensuring no waste of electricity. I'm new to reinforcement learning, and I'm not sure how to approach this complex problem. Any guidance would be greatly appreciated! submitted by /u/uonliaquat [link] [comments]  ( 9 min )

A few years ago, I remember reading a multipart series of articles/blog posts explaining how to develop agents for classical games. I believe the series started with tic-tac-toe and definitely progressed to gomoku, before maybe moving on to more complex games. I think there was more of a focus on algorithms (maybe MCTS) and concepts than code. It's a long shot, but does anyone recall this series or know if it's archived somewhere? seems like it might have been taken down. Wasn't on Medium. I think it might've been a personal website. I vaguely remember a green UI theme? submitted by /u/nothymn [link] [comments]  ( 8 min )

Curious what people's stance on this is. Why? View Poll submitted by /u/philippemnoel [link] [comments]  ( 8 min )

submitted by /u/Philipp [link] [comments]  ( 8 min )

Deutsche Telekom, e&, SK Telecom (SKT), and Singtel penned an agreement to form a global telecoms AI alliance designed to use the technology to unlock new business opportunities and accelerate industry growth.[1] Influencers Lil Miquela, Imma, and supermodel Shudu have raked in millions from deals with fashion giants such as Dior, Calvin Klein, Chanel, and Prada. But these shiny celebrities all have one thing in common — not one of them is real.[2] Google’s chatbot Bard reveals the jobs most at risk of artificial intelligence with truck drivers and data entry clerks on the list – while teachers and lawyers are among the safest careers.[3] DoorDash Inc., the US food-delivery service that competes with Uber Technologies Inc. and GrubHub, is looking to speed up ordering and help customers find food options with an artificial intelligence-based chatbot.[4] Sources: [1] https://www.mobileworldlive.com/featured-content/home-banner/global-operator-giants-launch-ai-alliance/ [2] https://www.the-sun.com/tech/8725778/ai-influencers-fashion-deals/ [3] https://www.dailymail.co.uk/news/article-12354605/googles-AI-bard-predicts-jobs-risk.html [4] https://www.bloomberg.com/news/articles/2023-07-27/doordash-is-working-on-an-ai-chatbot-to-speed-up-food-ordering?in_source=embedded-checkout-banner submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 billion to go through disease target identification, drug screening, drug-target validation, and eventual commercial launch. […]  ( 15 min )

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and mechanisms behind customer purchase decisions can facilitate revenue growth. However, the loss of customers (commonly referred to as customer churn) always poses a risk. Gaining insights into why customers leave can […]  ( 14 min )

Let’s image – with algorithms and a nerdy charm that could melt any data center, an ‘AI’ wearing lab coats and stethoscopes patrolling hospital hallways, tirelessly monitoring patients. The digital doctor will take the pulse of Mother Earth and reduce waste, cut energy consumption, and cut energy consumption! The artificial intelligence community is well aware… Read More »Doctor AI: Healing humans and mother earth hand in hand The post Doctor AI: Healing humans and mother earth hand in hand appeared first on Data Science Central.  ( 20 min )

In an age where efficiency is king, manufacturing firms are in a constant race to outshine their competition. Imagine if you could boost productivity, slash downtime, and cut costs all at once. Sounds like a dream, right? The good news is, this isn’t a fantasy. It’s achievable through Internet of Things (IoT) solutions. IoT solutions… Read More »Increase efficiency of manufacturing operations with IoT solutions The post Increase efficiency of manufacturing operations with IoT solutions appeared first on Data Science Central.  ( 21 min )

“If you start by creating your data, then it’s like you are piling up some value or you’re creating some assets,” WordLift CEO Andrea Volpini told me in our recent FAIR Data Forecast interview. Volpini’s an advocate for adding structured data such as Schema.org to your content. That way, the content becomes logically connected and… Read More »Human-centered data networking with interpersonal knowledge  graphs The post Human-centered data networking with interpersonal knowledge  graphs appeared first on Data Science Central.  ( 21 min )

submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )

“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.  ( 10 min )

Could you please help us get more responses for this study? As part of my PhD research project at Applied Artificial Intelligence Institute of Deakin University, we are investigating the challenges that software engineers face when working with machine learning (ML) models in production. Moreover, we explore how to enhance our proposed solution to better meet the needs of these engineers. ​ The objective of this study is to pinpoint the areas where software engineers need more support and resources to effectively work with ML components in production. It also aims to evaluate the effectiveness of a proposed protocol to improve software engineers' productivity and enable them to work more effectively with ML components in production environments. ​ With the knowledge gained from this i…  ( 9 min )

Hello everyone. I am trying to replace the GloVe embeddings based model outlined in this paper by BERT embeddings. The authors of the paper have trained their model for 250 epochs, which for what I am doing is not feasible. I was wondering what would be the recommended number of epochs I should run the BERT model for? I know it is a pretty open ended question, but I was looking to get the community's view on how much epochs should a BERT based model be trained for. Any information will be much appreciated. submitted by /u/nocturnal_1_1995 [link] [comments]  ( 9 min )

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I think it’s clear that Hugging Face is not aligned to the OSS community any more and it’s only going to get worse over the next few years. What are the top alternatives or where should the OSS contributors go? I’m trying to think ahead to what libraries we should rely on and contribute to. Anyone else have this as a worry? https://twitter.com/untitled01ipynb/status/1685667451197878272 submitted by /u/homunculAI [link] [comments]  ( 8 min )

🚀 Code: https://github.com/sdc17/UPop 📑 Paper: https://proceedings.mlr.press/v202/shi23e/shi23e.pdf 🧐 A Quick Look What is it: UPop is the first structured pruning framework for vision-language Transformers. It enables effective structured pruning on various multi-modal & uni-modal tasks (including Visual Reasoning, Image Captioning, Visual Question Answer, Image-Text Retrieval, Text-Image Retrieval, Image Classification and Image Segmentation), datasets (including NLVR2, COCO Caption, VQAv2, COCO, Flickr30K, ImageNet and ADE20K), and model architectures (including BLIP, CLIP, DeiT and Segmenter). https://preview.redd.it/gfbjnxjm95fb1.png?width=2145&format=png&auto=webp&s=108898690f66a1f0afa068b69487859213055928 What challenge does it tackle: The above figure demonstrates that Unified Search adopted by UPop rescues us from the burden of repeated experiments (e.g., doing grid search) for searching optimal compression ratios among different modalities and structures. Furthermore, Progressive Pruning adopted by UPop eliminates the weight gap between the searched model and the pruned subnet to be retrained, therefore gaining better convergence and performance, especially at high compression ratios. How about the performance: On multimodal tasks, for example, UPop can achieve 2x compression with only 1.2% and 2.0% accuracy loss on the VQAv2 dataset for Visual Question Answer and the NLVR2 dataset for Visual Reasoning, respectively. On unimodal tasks, for example, UPop can achieve 1.5x and 1.2x compression without any loss of accuracy on the ImageNet dataset for Image Classification and the ADE20K dataset for Image Segmentation, respectively. Some examples of vector-level structured granularity are as follows. https://preview.redd.it/lifz1n1ia5fb1.png?width=1187&format=png&auto=webp&s=f419d9c5fb4d80a2a564198eba356021e1c275e4 submitted by /u/Salty-Situation2606 [link] [comments]  ( 9 min )

​ Title Company Location URL Senior Software Engineer (Backend) Nova Credit Remote https://pycareer.io/jobs/6816 Data Scientist - Delivery, Senior-Staff Instacart Not Specified https://pycareer.io/jobs/6773 Data Scientist Data Scientist United States https://pycareer.io/jobs/6780 Senior Data Scientist (NLP and Classification Expert) › Senior Data Scientist (NLP and Classification Expert) › Not Specified https://pycareer.io/jobs/6781 Senior Software Engineer (Backend) Senior Software Engineer (Backend) United States https://pycareer.io/jobs/6788 AWS Data Engineer Apply Not Specified United States https://pycareer.io/jobs/6801 Senior Data Engineer Manager Apply Not Specified United States https://pycareer.io/jobs/6802 Data Scientist – Delivery, Senior-Staff Instacart Instacart Remote https://pycareer.io/jobs/6805 Software Design Engineer – NET, Python – Citizen/GC (H) Not Specified Remote https://pycareer.io/jobs/6837 Senior Data Scientist at Getty Images Getty Images Remote https://pycareer.io/jobs/6839 Lead Data Scientist at General Mills General Mills Remote https://pycareer.io/jobs/6840 Data Scientist – Delivery, Senior-Staff at Instacart Instacart Remote https://pycareer.io/jobs/6842 ​ submitted by /u/tadasg6 [link] [comments]  ( 9 min )

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For a master thesis I want to write a ML model (and hopefully make my own contribution) and I plan to use macroeconomic data. I could predict the typical inflation, GDP, unemployment, but are there any other factors that are important. Could you give me some ideas. Thanks! submitted by /u/AnyJello605 [link] [comments]  ( 8 min )

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Everyone is going crazy creating LORAs and fine-tuning huge LLMs, however I've seen many suggesting that models such as T5 from Google has its place in the enterprise. Have you guys used this or similarly small models for any novel real world problems? Please do share! submitted by /u/MonkeyMaster64 [link] [comments]  ( 8 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 8 min )

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Hi I am new to computer vision, I am working on a particular hackathon challenge, where the input labels are in COCO format. I am using the following code to generate masks, cat_ids = coco.getCatIds() anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None) anns = coco.loadAnns(anns_ids) anns_img = np.zeros((img['height'],img['width'])) for ann in anns: anns_img = np.maximum(anns_img,coco.annToMask(ann)*ann['category_id']) But the image has overlapping labels for some pixels, and this masking will only assign one label for such pixel, resulting in information loss, each there any way to prevent this and preserve the information? submitted by /u/franticpizzaeater [link] [comments]  ( 9 min )

Hi, y’all. So, I completed my masters degree. Got a programming job. I’m amazed at the capabilities of machine learning and want to build my own models. I don’t really want to go and get another degree, but want to learn how to build models. I’m particularly interested in forecasting because my job deals with NASA and wind data. I’m wondering if we could predict 6 hour wind data with a balloon sounding. I know c++ and python. How do I stay relevant to the changing technology space and learn how to build some cool stuff that may be useful? Thanks for any advice. submitted by /u/corey4005 [link] [comments]  ( 9 min )

Hello everyone, I have been asked the following question related to SVM (Hard Margin) in the exam, and I failed to answer it. Can anyone help me find the solution? My approach was to sketch it and draw the marginal plane, then identify support vectors using my intuition. After that, I created a hyperplane that was the midpoint of both marginal planes, found its slope and y-intercept, but still, my answer was wrong. I am very new to machine learning, so any help would be appreciated. Consider the dataset M = {((1, 0)^T, 1), ((0, −1)^T, 1), ((1, −1)^T, −1), ((2, 0)^T, −1)}. Determine w and b. submitted by /u/salman_ml [link] [comments]  ( 9 min )

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has anyone here ever created or worked with or even seen or come across any ai models about the art market? I am not talking about artists or the art itself- but any kind of model about the art market (since it's such an economic enigma and different from normal markets) submitted by /u/Icy-Bid-5585 [link] [comments]  ( 8 min )

Original logo “What species of bird is that?” New logo “Why does it have a troll Face?” “I think it's a picture of someone who looks like a troll with the face of an emoji!” I don’t see it but it makes sense somehow 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

I swear this wasn't the case just a day or two ago, and I haven't seen it mentioned, but I'm now seeing a file upload button in Poe, regardless of what the language model is! Screenshot I uploaded the PDF of the recently scientific paper by the Korean research group claiming to have discovered a room temperature semiconductor, in the original Korean, and asked various language models whether they thought the methodology is legit, and each bot I tried was able to read the PDF. I tried Claude-instant, Claude2, 'Assistant' (Poe's own GPT based bot that claims to have its own training dataset), PaLM, ChatGPT 3.5, and ChatGPT4. Poe also has three versions of the recently released Llama model by Meta. It gave me an error when I tried to ask it about the PDF attachment, but I was able to upload a text document and it was able to read it fine. Screenshot of Claude-instant evaluating PDF Screenshot of Google PaLM evaluating PDF Screenshot of Llama-2-70b evaluating text file containing song lyrics It also works with custom bots. Here's me trying it out with a 'Truth Checker' bot I made (based on Claude-Instant). Here it is using a Claude-2 based version of the TruthChecker bot. (Here's the link to the TruthChecker bot if you have Poe and wanna check it out: https://poe.com/TruthChecker) Edit: I can see here how the context size matters... for instance, Claude-Instant only has a context size of about 7k words, so it clearly can't read the whole paper, while Claude-2 can and gives a very different answer... TL:DR; looks like Poe.com allows file attachment/upload on all language models now. No idea what filetypes are supported. submitted by /u/AnticitizenPrime [link] [comments]  ( 9 min )

As knowledge management (KM) leaders and practitioners, it’s critical to have an active role in guiding the integration of generative AI into KM areas, applications, and processes. I'm seeking some guidance on the current state of generative AI integration within the KM context. Specifically, answering the following question: Where and how generative AI is accelerating and impacting knowledge use cases, areas, and processes? Please let me know what you think. submitted by /u/rachadbn [link] [comments]  ( 8 min )

I Tried An AI To Extend Music But It Didnt Really Go Well And Im Not Planning To Pay $12 To Extend Some Music For Fun So Are There Any Good Music Extension AIs Out There (Creates New Music Based On A MP3 File Provided) submitted by /u/KXRulesYT [link] [comments]  ( 8 min )

I'm trying to find an AI tool to help me test out some home renovation, but everything i find is either just for reimagining one room at a time or for generating brand new floor plans. I specifically want to look at some options for merging my kitchen and living room. Preferably free or freemium. Any suggestions? submitted by /u/litari [link] [comments]  ( 8 min )

I previously posted a query about the same, but when i tried to implement A2C model training using SB3 on simple spread environment, I am not getting good and improved reward values, it's still highly negative and the model is performing rather randomly. env = ss.pettingzoo_env_to_vec_env_v1(env) env = ss.concat_vec_envs_v1(env, 4, num_cpus=2, base_class="stable_baselines3") policy_kwargs = dict(net_arch = [128,128]) model = A2C( MlpPolicy, env, verbose=1, learning_rate= 0.007, gamma = 0.95, ent_coef = 0.4, policy_kwargs= policy_kwargs, tensorboard_log= logdir ) This is a fragment of code for reference. I tried to give more policy_kwargs like: share_features_extractor=False, or even tried to implement entirely custom policy, but the total average reward is still not going above -300. Also, the tensorboard plots are not showing ep_rew_mean plot, should I be passing some parameters for that? submitted by /u/bruhhhwhats [link] [comments]  ( 9 min )

In AlphaGo, a tree search is performed, and uses the policy network to reduce the breadth of it. At the leafs, if the states are not terminal, it uses the value network. And then "backup" the values to update the Q value at the initial state (if 70% of my rollouts won after performing action a_1, my Q value q(initial_state, a_1) should converge to 0.7 in my initial state). But I don't see where the policy network is updated? ​ Here is a slide from David Silver, the first-author of AlphaGo, but it doesn't mention how to update the policy network. ​ https://preview.redd.it/f29no3xe64fb1.png?width=1523&format=png&auto=webp&s=5adb312b1d0c033aa8ebb328197fd7d917724f06 Have I missed something? Thankss! submitted by /u/Potential_Biscotti14 [link] [comments]  ( 9 min )

recently, I've been trying to make a deep q network for solving 2x2 rubik's cubeBut after months, I stuck with same output for HUNDREDS of times :( I tried everything: change learning rate, change discout factor but no luck Here's update rule: newQ=currentQ+alpha*(newR+gamma*max(futureQValue.flatten().tolist())-currentQ) import torch import torch.nn as nn import torch.optim as optimizer import os from tqdm import tqdm class DQN(nn.Module): def __init__(self, stateSpaceSize,actionSpaceSize): super(DQN, self).__init__() self.fc1=nn.Linear(stateSpaceSize,128) self.fc2=nn.Linear(128,128) self.fc3=nn.Linear(128,128) self.fc4=nn.Linear(128,128) self.fc5=nn.Linear(128,128) self.fc6=nn.Linear(128,actionSpaceSize) def forward(self, x): self.relu=nn.ReLU() self.sigmold=nn.Sigmoid() self.LeakyReLU…  ( 9 min )

I need a google colab with reinforcement learning trained to detect anomalies in computer network traffic. submitted by /u/Unable_Blacksmith_81 [link] [comments]  ( 8 min )

Hi Guys, I am working on a nn model, which can help automate the building of APIs. The problem is, we are moving data in which, there are thousands of fields, however, the fields between systems are similar in nature. This to me seems like an easy classification problem, however it doesn't scale the best. ​ In terms of the data I have, if I have a dataset of 10 systems, there are not enough examples for each class for the model to train well. That is with a simple classifier where every field is a class. ​ I was also thinking of using a Siamese model, where I compare the similarity between them, which allows me to use my more limited dataset more effectively ​ I was wondering if there are any more architectures you guys think I should consider, or will be helpful in solving my problem ​ Thank you for your help! submitted by /u/eatlantis [link] [comments]  ( 9 min )

ARPAbet is a phonetic spelling system developed by— you guessed it—ARPA, before it became DARPA. The ARPAbet system is less expressive than IPA, but much easier for English speakers to understand. Every sound is encoded as one or two English letters. So, for example, the sound denoted ʒ in IPA is ZH in ARPAbet. In […] ARPAbet and the Major mnemonic system first appeared on John D. Cook.  ( 6 min )

Hi all. I (believe) I have a tensorflow NN set up to learn how to intercept a target moving in the x-y plane. Right now, the agent can choose to change its velocity by a small amount in any of the 3 directions (for the 3D case later), then the simulation updates the agents position. The state of the sim is the relative distance and velocity vectors between the target and the pursuer. I am confused how to set up a reward function, however. When I first set it up to be a reward of 1/R (R being the distance magnitude between the target and pursuer) to reward for shorter distances and give less reward for further distances as well as a very large reward when a collision occurred, it seemed like the rewards converged to a small value instead of getting larger. Any advice? I'd be willing to upload a github link as well if you wanted to look at the code submitted by /u/Happylightsocket [link] [comments]  ( 9 min )

I am trying to implement my own version of ppo using gymnasium. Here is my code for rollout - def rollout(): transitions = [] disc_reward_list = [] for i in range(ppo_batch): obs = torch.tensor(env.reset(), dtype=torch.float32) print("obs = ", obs.shape) all_rewards = [] iter = 0 done = False tot_rewards = 0 print("done = ", done) while True: act_probs = torch.distributions.Categorical(actor(obs.to(device)).squeeze()) print("act_probs = ", act_probs) # print("act_probs = ", actor(obs.to(device))) action = act_probs.sample().squeeze() action = action.cpu().detach().numpy() print("action shape = ", action.shape) next_state, reward, done, info = env.step(action) print("next_state shape = ", next_state.shape) print("reward shape = ", reward.shape) print("done shape = ", done) action = torch.tensor(action, dtype=torch.float32).to(device) all_rewards.append(reward) tot_rewards += reward iter += 1 transitions.append((obs.cpu().detach().numpy(), action.cpu().detach().numpy(), act_probs.log_prob(action).cpu().detach().numpy())) obs = torch.tensor(next_state, dtype=torch.float32).unsqueeze(0) print("Reward = ", tot_rewards) eps_rew = 0 eps_rew_list = [] for reward in reversed(all_rewards): eps_rew = eps_rew*gamma + reward eps_rew_list.append(eps_rew) for rtgs in reversed(eps_rew_list): disc_reward_list.append(rtgs) My issue is that in my while loop - The environment autoresets after the `done` variable becomes `True` For instance, if I have `8` environments running in parallel `env=gym.vector.make('CartPole-v1', num_envs=8)` and print out the done shape, I might get - `[False False False False False True False False]`. I want that environment where `done=True` to stop and not reset. I believe that's how PPO is supposed to work. I am a bit of a beginner with this stuff. Please let me know if something I said is not clear. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )

Can someone please point me to resources how distributed actor-critic algorithms work? My final goal is to understand distributed PPO works. I was following thisblog and a few other books but I'm unable to see the big picture nor am I able to understand the little details. The big picture - Why does distributed training help in online algorithms like PPO, Actor-Critic The code details - I figured out how to make multiprocessing work with gym. But how does one perform learning? Should I combine all the parallel environments and feed them to my neural network? I checked cleanrl but am getting a little confused. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

​ ​ I know that `SB3` provides various techniques to come up with vectorized environments. I want to limit myself to only using the vectorized environments and implement the RL algorithms from scratch. Would that be possible? My final objective is to learn how to play with RL hyperparameters on parallel environments in order to accelerate learning speeds. Currently, I am stuck on - import os import gymnasium as gym from stable_baselines3.common.vec_env import DummyVecEnv env = DummyVecEnv([lambda: gym.make("CartPole-v1")]) obs = env.reset() done = False while not done: action = env.action_space.sample() next_obs, reward, done, info = env.step(action) obs = next_obs But I get the following error - ​ Traceback (most recent call last): File "D:\q_learning\dummy_envs.py", line 9, in next_obs, reward, done, info = env.step(action) File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\base_vec_env.py", line 197, in step return self.step_wait() File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\dummy_vec_env.py", line 59, in step_wait self.actions[env_idx] IndexError: invalid index to scalar variable. ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hi everyone, I'm messaging on behalf of a brand new AI based Social Media app called Cantina. It's like a cross between the best parts of Discord, Twitch, and Snapchat, and uses both Stable Diffusion and ChatGPT to allow users to create and interact with AI bots. The app is currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots, and these bots also work as AI art creators. Simply ask them to draw something, and they'll provide you with a picture based on your prompt. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to message me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

Anyone know if there is anything able to describe the content of a video? I have found a lot of stuff for images but nothing for videos. submitted by /u/crazewill [link] [comments]  ( 8 min )

I plan on using Machine Learning Algorithms to forecast future values of power demand and the literature on the subject is a bit divisive. I'm getting ANN, Decision Trees (odd), SVMs etc. I just want to know what models you guys would use (MATLAB and Python only, except it's really good). Thank you in anticipation. P.S: Any literature to streamline my search will be greatly appreciated. submitted by /u/X69-2 [link] [comments]  ( 8 min )

Paper: https://arxiv.org/abs/2307.13528 Blog: https://ethanc111.github.io/factool_website/ Github: https://github.com/GAIR-NLP/factool Factool is a tool augmented framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Factool now supports 4 tasks: knowledge-based QA: Factool detects factual errors in knowledge-based QA. code generation: Factool detects execution errors in code generation. mathematical reasoning: Factool detects calculation errors in mathematical reasoning. scientific literature review: Factool detects hallucinated scientific literatures. Abstract: The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in t…  ( 9 min )

Hello fellow coders and AI enthusiasts! First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟 Back in 2022, we were the first one to tackle the common challenge of uncontrolled, unstructured outputs from large language models like GPT-3. , and your support has pushed us to keep improving.Today, we're thrilled to share some major updates that make Promptify even more powerful https://preview.redd.it/hk7ro4tmnyeb1.png?width=1510&format=png&auto=webp&s=226ada1f896c620137f827932c03a9df88e35d69 ​ Unified Architecture 🧭: Introducing Prompter, Model & Pipeline Solution Detailed Output Logs 📔: Comprehensive structured JSON format output within the log folder. Wider Model Support 🤝: Supporting models from OpenAI, Azure, Cohere, Anthropic, Huggingface and more - think of it as your universal language model adapter. Robust Parser 🦸‍♂️: Parser to handle incomplete or unstructured JSON outputs from any LLMs. Ready-Made Jinja Templates 📝: Jinja prompt templates for NER, Text Classification, QA, Relation-Extraction, Tabular data, etc. Database Integration 🔗: Soon, Promptify directly to Mongodb integration. Stay tuned! Effortless Embedding Generation 🧬: Generate embeddings from various LLMs effortlessly with the new update. https://preview.redd.it/rf8yjqxnnyeb1.png?width=2160&format=png&auto=webp&s=87b7c2408382757e38ff554fde56e56bd60b1793 ​ Check out the examples and take Promptify for a spin on GitHub. If you like what you see, we'd be honored if you gave us a star! Github: https://github.com/promptslab/Promptify Colab: Try Now on Colab Explore Other Cool Open Source LLM Tools: https://github.com/promptslab Join 1.6k+ Promptify users on Discord to dive deep into prompt engineering, discuss the latest with LLMs, and advance NLP research together: https://discord.com/invite/m88xfYMbK6 Thank you again for your support - here's to more structured AI! submitted by /u/StoicBatman [link] [comments]  ( 9 min )

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Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )

A conclusion of the no free lunch theorem is that there can't exist a universal learning algorithm. My understanding has been that this was the end goal of AI research; creating a universal learner. What is the community progressing towards, if not that? submitted by /u/lemlo100 [link] [comments]  ( 8 min )

Hello everyone, I'm currently working on an exciting project using the Travelling Salesman Problem (TSP), and I'd love to have some coding wizards join the fun! If you enjoy solving optimisation problems and have some coding experience, particularly in Python, this project is for you. To determine the most efficient routes, we'll use heuristic methods such as the Nearest Neighbour Algorithm, Genetic Algorithm, and Ant Colony Optimisation. If you aren't a TSP expert yet, don't worry. We'll be learning and exploring together! I'm really looking forward to seeing how we can optimise routes for real-world applications like delivery and travel planning. So, if you're looking for a coding adventure and want to be a part of a fantastic project, hit me up! Let's crack this TSP puzzle and create some smart solutions. If you're interested in collaborating, please send me a message. I can't wait to work with you and nerd out on some fantastic code! submitted by /u/vampire_19 [link] [comments]  ( 9 min )

I am trying to make a machine that could detect if there is any bacteria in blood through pictures. However I do not know any thing about machine learning and only knows a little bit of Python and C++. What should I do? submitted by /u/EthanWasTakenAgain [link] [comments]  ( 8 min )

I am currently working on my IB Extended Essay, and I would greatly appreciate your help in gathering valuable insights from individuals knowledgeable in the field of AI. The purpose of my survey is to understand the perspectives of AI enthusiasts . If you have a few minutes to spare, I kindly request you to participate in my survey. Your input will contribute significantly to my research and help me gain a deeper understanding of the topic. The survey covers various aspects of AI, and your expertise will be invaluable in shaping the results. Survey Link: https://forms.gle/PVGrRbPLTpZRbbpL9 Rest assured that all responses will be kept confidential and only used for academic purposes. Additionally, feel free to share this survey with others who might be interested or knowledgeable in the field. Thank you in advance for your time and contributions! Your participation will greatly aid in the successful completion of my IB Extended Essay. submitted by /u/KVNG_Winston [link] [comments]  ( 9 min )

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Please suggest me efficient LASSO regression implementations for very high dimensional data. Thanks in advance! submitted by /u/Charming-Witness-286 [link] [comments]  ( 8 min )

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Hi everyone, I'm working with a brand new AI based Social Media app called Cantina. It's currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. For example: I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner! There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

So, long story short. I lost BOTH my day jobs because of AI. Initially I was bitter that AI "took my job," but after pulling up my socks, I found dozens of new opportunities thanks to AI. Somewhat ironically, I found a new job at an AI Social Media app called Cantina, and I couldn't be more excited. Cantina is best described as a mix up of the best parts of Discord, Twitch, and Snapchat... with the unique bonus of being able to interact with and build your own AI chat bots. Sort of hard to explain, but once you try it out you'll get the idea. The app is currently in a limited INVITE ONLY Beta phase and I'm looking to invite a small number of users to give it a shot (currently iOS only, but Android is coming soon!). Here's an invite so you can dive in and see what it's all about: https://canti.na/dIdKzWcEpBb After joining, you'll find lots of rooms you can join and chat through any combination of voice, video, or text. And if no rooms stand out, you can make your own! There are lots of premade bots that you can interact with or add to rooms, and you can easily create your own bot using the Make A Bot function. This is the standout feature, and I'm simply blown away at what's possible. I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who is an algamation of both Ronald McDonald and Donald Trump and WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner. I still can't believe I'm getting paid to do this... Anyway, please take a moment to download and check it out, and if you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. ​ submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )

The latest article published by Google Deepmind is seriously approaching a Blade Runner type future. Their research paper is on the first VLA (vision-language-action) Model RT-2 (see paper), a multi-modal algorithm which tokenizes robotic inputs and output actions (e.g., camera images, task instructions, and motor commands) in order to use this information to learn quickly by translating the knowledge it receives in real-time into generalized instructions for its own robotic control. RT-1 absorbs large amounts of data, including robot trajectories with multiple tasks, objects and environments, resulting in better performance and generalization. (source) RT-2 incorporates chain-of-thought to allow for multi-stage semantic reasoning, like deciding which object could be used as an improvise…  ( 10 min )

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Google introduces Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalized instructions for robotic control, while retaining web-scale capabilities.[1] Thymia, a healthtech startup building gamified AI tools to revolutionize how we assess and monitor mental health, has today announced a €2.4 million seed round to expand the reach and capabilities of its pioneering technology.[2] Intel CEO Pat Gelsinger was very bullish on AI during the company’s Q2 2023 earnings call — telling investors that Intel plans to “build AI into every product that we build.”[3] Walmart is using artificial intelligence to help streamline their product organization.[4] Sources: [1] https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action [2] https://www.eu-startups.com/2023/07/london-based-thymia-raises-e2-4-million-seed-round-to-expand-its-video-game-inspired-mental-health-ai/ [3] https://www.theverge.com/2023/7/27/23810360/intel-pat-gelsinger-ai-every-platform-promise [4] https://www.nbcnews.com/nightly-news/video/walmart-using-ai-to-streamline-organization-what-will-it-mean-for-workers-189519429834 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Hi, guys! Hope everyone is doing fine. Some of you may remember me. About two months ago I posted here about an anxiety breakdown I've gone through regarding "AI", "Programming" and how "human programmers would end" and stuff like that, which was a major concern for me since programming was my job and my favorite thing to do. I was wondering for some time if I was supposed to share an update here. I decided to do so since somebody out there may be feeling the same as me. So I not only have an update but I also want to give some advice to whoever is going through this sh*thole. After that post, I talked about my feelings with a lot of people around me (friends and fiancée), and everyone was very supportive. At first I thought they would laugh at me, since there are a lot more to worry to…  ( 12 min )

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“RT-2 is the new version of what the company calls its vision-language-action (VLA) model. The model teaches robots to better recognize visual and language patterns to interpret instructions and infer what objects work best for the request.” submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

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One of the reasons that I moved back to Iowa last year was that I saw an opportunity to work with local educational institutions to create an AI Institute for organizations in middle America that either get overlooked in the AI conversation or are unsure what AI means to them. I wanted to reduce the… Read More »Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” The post Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” appeared first on Data Science Central.  ( 22 min )

A few days ago I wrote about Jaccard distance, a way of defining a distance between sets. The Ruzsa distance is similar, except it defines the distance between two subsets of an Abelian group. Subset difference Let A and B be two subsets of an Abelian (commutative) group G. Then the difference A − B […] Ruzsa distance first appeared on John D. Cook.  ( 6 min )

Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.

Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In this paper, we present a lightweight, fast, and high-performance regularization method for Mahalanobis distance-based uncertainty prediction, and that requires minimal changes to the network's architecture. To derive Gaussian latent representation favourable for Mahalanobis Distance calculation, we introduce a self-supervised representation learning method that separates in-class representations into multiple Gaussians. Classes with non-Gaussian representations are automatically identified and dynamically clustered into multiple new classes that are approximately Gaussian. Evaluation on standard OOD benchmarks shows that our method achieves state-of-the-art results on OOD detection with minimal inference time, and is very competitive on predictive probability calibration. Finally, we show the applicability of our method to a real-life computer vision use case on microorganism classification.

We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearby nodes, thereby failing to faithfully capture infinite repeating patterns and distant interatomic interactions. In this work, we propose several innovations to overcome these limitations. First, we propose to model physics-principled interatomic potentials directly instead of only using distances as in many existing methods. These potentials include the Coulomb potential, London dispersion potential, and Pauli repulsion potential. Second, we model the complete set of potentials among all atoms, instead of only between nearby atoms as in existing methods. This is enabled by our approximations of infinite potential summations with provable error bounds. We further develop efficient algorithms to compute the approximations. Finally, we propose to incorporate our computations of complete interatomic potentials into message passing neural networks for representation learning. We perform experiments on the JARVIS and Materials Project benchmarks for evaluation. Results show that the use of interatomic potentials and complete interatomic potentials leads to consistent performance improvements with reasonable computational costs. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet).

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.

Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches face the data and workload drift problem for a long time. Although both query-driven and hybrid methods are proposed to avoid this problem, even the state-of-art of them suffer from high training and estimation costs, limited scalability, instability, and long-tailed distribution problem on high cardinality and high dimensional tables, which seriously affects the practical application of learned cardinality estimators. In this paper, we prove that most of these problems are directly caused by the widely used progressive sampling. We solve this problem by introducing predicates into the autoregressive model and propose Duet, a stable, efficient, and scalable hybrid method to estimate cardinality directly without sampling or any non-differentiable process, which can not only reduces the inference complexity from $O(n)$ to $O(1)$ compared to Naru and UAE but also achieve higher accuracy on high cardinality and high dimensional tables. Experimental results show that Duet can achieve all the design goals above and be much more practical and even has a lower inference cost on CPU than that of most learned methods on GPU.

Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. "scheduling a doctor's appointment without a phone". While current approaches show encouraging results using large language models (LLMs), they are hindered by drawbacks such as costly API calls and reproducibility issues. In this paper, we advocate planning using smaller language models. We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (counterfactual) planning capabilities. More concretely, we develop symbolic procedural knowledge distillation to enhance the implicit knowledge in small language models and an inference-time algorithm to facilitate more structured and accurate reasoning. In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation. In both the original and counterfactual setting, we show that orders-of-magnitude smaller models (770M-11B parameters) can compete and often surpass their larger teacher models' capabilities.

Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in such representations remains poorly understood. In this work, techniques for non-intrusive prediction of SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found that self-supervised representations are useful as input features to non-intrusive prediction models, achieving competitive performance to more complex systems. A detailed analysis of the performance depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals

This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. It can also be considered as primary step of a fact-checking system. Our best-performing method took advantage of an ensemble classification scheme centered on Model Souping. When applied to the English data set, our submitted model achieved an overall F1 score of 0.878 and was ranked as the second-best model in the competition.

We present Factor Fields, a novel framework for modeling and representing signals. Factor Fields decomposes a signal into a product of factors, each represented by a classical or neural field representation which operates on transformed input coordinates. This decomposition results in a unified framework that accommodates several recent signal representations including NeRF, Plenoxels, EG3D, Instant-NGP, and TensoRF. Additionally, our framework allows for the creation of powerful new signal representations, such as the "Dictionary Field" (DiF) which is a second contribution of this paper. Our experiments show that DiF leads to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, DiF enables generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from sparse observations and few-shot radiance field reconstruction.

The option of sharing images, videos and audio files on social media opens up new possibilities for distinguishing between false information and fake news on the Internet. Due to the vast amount of data shared every second on social media, not all data can be verified by a computer or a human expert. Here, a check-worthiness analysis can be used as a first step in the fact-checking pipeline and as a filtering mechanism to improve efficiency. This paper proposes a novel way of detecting the check-worthiness in multi-modal tweets. It takes advantage of two classifiers, each trained on a single modality. For image data, extracting the embedded text with an OCR analysis has shown to perform best. By combining the two classifiers, the proposed solution was able to place first in the CheckThat! 2023 Task 1A with an F1 score of 0.7297 achieved on the private test set.

Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

We present computational and experimental results on how artificial intelligence (AI) learns to control an Acrobot using reinforcement learning (RL). Thereby the experimental setup is designed as an embedded system, which is of interest for robotics and energy harvesting applications. Specifically, we study the control of angular velocity of the Acrobot, as well as control of its total energy, which is the sum of the kinetic and the potential energy. By this means the RL algorithm is designed to drive the angular velocity or the energy of the first pendulum of the Acrobot towards a desired value. With this, libration or full rotation of the unactuated pendulum of the Acrobot is achieved. Moreover, investigations of the Acrobot control are carried out, which lead to insights about the influence of the state space discretization, the episode length, the action space or the mass of the driven pendulum on the RL control. By further numerous simulations and experiments the effects of parameter variations are evaluated.

Many properties in the real world, such as desirability or strength in competitive environment, can't be directly observed, which makes them difficult to evaluate. To deal with this challenging problem, prior works have primarily focused on estimating those properties of known items, especially the strength of sports players, only of those who appears in paired comparison dataset. In this paper, we introduce Deep Bradley-Terry Rating (DBTR), a novel ML framework to evaluate any properties of unknown items, not necessarily present in the training data. Our method seamlessly integrates traditional Bradley-Terry model with a neural network structure. We also generalizes this architecture further for asymmetric environment with unfairness, which is much more common in real world settings. In our experimental analysis, DBTR successfully learned desired quantification of those properties.

The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that by employing neural networks we can quantify the degree of entanglement without needing to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result employing networks trained using exclusively simulated data. Finally, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.

Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched representations have been observed to exhibit many robust performance characteristics that are known to occur when a data sample comes from a sub-gaussian random design, which is a powerful statistical model of data distributions. However, this phenomenon has only been studied for specific tasks and metrics, or by relying on computationally expensive methods. We address this by providing an algorithmic framework for gaussianizing data distributions via averaging, proving that it is possible to efficiently construct data sketches that are nearly indistinguishable (in terms of total variation distance) from sub-gaussian random designs. In particular, relying on a recently introduced sketching technique called Leverage Score Sparsified (LESS) embeddings, we show that one can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll N$, that is nearly indistinguishable from a sub-gaussian design, in time $O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of non-zero entries in $A$. As a consequence, strong statistical guarantees and precise asymptotics available for the estimators produced from sub-gaussian designs (e.g., for least squares and Lasso regression, covariance estimation, low-rank approximation, etc.) can be straightforwardly adapted to our sketching framework. We illustrate this with a new approximation guarantee for sketched least squares, among other examples.

We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on the coordinate system used to describe them, it is desirable to encode such symmetries in the neural operator architecture for better performance and easier learning. While encoding symmetries in the physical domain using group theory has been studied extensively, how to capture symmetries in the frequency domain is under-explored. In this work, we extend group convolutions to the frequency domain and design Fourier layers that are equivariant to rotations, translations, and reflections by leveraging the equivariance property of the Fourier transform. The resulting $G$-FNO architecture generalizes well across input resolutions and performs well in settings with varying levels of symmetry. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.

Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.

One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://yanweiw.github.io/noise2ptz

Medical radiography segmentation, and specifically dental radiography, is highly limited by the cost of labeling which requires specific expertise and labor-intensive annotations. In this work, we propose a straightforward pre-training method for semantic segmentation leveraging Denoising Diffusion Probabilistic Models (DDPM), which have shown impressive results for generative modeling. Our straightforward approach achieves remarkable performance in terms of label efficiency and does not require architectural modifications between pre-training and downstream tasks. We propose to first pre-train a Unet by exploiting the DDPM training objective, and then fine-tune the resulting model on a segmentation task. Our experimental results on the segmentation of dental radiographs demonstrate that the proposed method is competitive with state-of-the-art pre-training methods.

Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$). However, in real-world applications, this $i.i.d.$ assumption often fails to hold due to unforeseen distributional shifts, leading to considerable degradation in model performance upon deployment. This observed discrepancy indicates the significance of investigating the Out-of-Distribution (OOD) generalization problem. OOD generalization is an emerging topic of machine learning research that focuses on complex scenarios wherein the distributions of the test data differ from those of the training data. This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Our discussion begins with a precise, formal characterization of the OOD generalization problem. Following that, we categorize existing methodologies into three segments: unsupervised representation learning, supervised model learning, and optimization, according to their positions within the overarching learning process. We provide an in-depth discussion on representative methodologies for each category, further elucidating the theoretical links between them. Subsequently, we outline the prevailing benchmark datasets employed in OOD generalization studies. To conclude, we overview the existing body of work in this domain and suggest potential avenues for future research on OOD generalization. A summary of the OOD generalization methodologies surveyed in this paper can be accessed at this http URL

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.

Weighted low rank approximation is a fundamental problem in numerical linear algebra, and it has many applications in machine learning. Given a matrix $M \in \mathbb{R}^{n \times n}$, a weight matrix $W \in \mathbb{R}_{\geq 0}^{n \times n}$, a parameter $k$, the goal is to output two matrices $U, V \in \mathbb{R}^{n \times k}$ such that $\| W \circ (M - U V^\top) \|_F$ is minimized, where $\circ$ denotes the Hadamard product. Such a problem is known to be NP-hard and even hard to approximate assuming Exponential Time Hypothesis [GG11, RSW16]. Meanwhile, alternating minimization is a good heuristic solution for approximating weighted low rank approximation. The work [LLR16] shows that, under mild assumptions, alternating minimization does provide provable guarantees. In this work, we develop an efficient and robust framework for alternating minimization. For weighted low rank approximation, this improves the runtime of [LLR16] from $n^2 k^2$ to $n^2k$. At the heart of our work framework is a high-accuracy multiple response regression solver together with a robust analysis of alternating minimization.

Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.

Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle. Such models, which predict drivers' continuous actions from their states, are particularly relevant for closing the gap between AD agent simulations and reality. To this end, we adapt two flexible quantile learning frameworks for this setting that avoid strong distributional assumptions: (1) quantile regression (based on the titled absolute loss), and (2) autoregressive quantile flows (a version of normalizing flows). Training happens in a behavior cloning-fashion. We use the highD dataset consisting of driver trajectories on several highways. We evaluate our approach in a one-step acceleration prediction task, and in multi-step driver simulation rollouts. We report quantitative results using the tilted absolute loss as metric, give qualitative examples showing that realistic extremal behavior can be learned, and discuss the main insights.

We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this question and presented negative results showing there exist data distributions where for several fairness constraints, any proper learner will exhibit high vulnerability when group sizes are imbalanced. Here, we present a more optimistic view, showing that if we allow randomized classifiers, then the landscape is much more nuanced. For example, for Demographic Parity we show we can incur only a $\Theta(\alpha)$ loss in accuracy, where $\alpha$ is the malicious noise rate, matching the best possible even without fairness constraints. For Equal Opportunity, we show we can incur an $O(\sqrt{\alpha})$ loss, and give a matching $\Omega(\sqrt{\alpha})$lower bound. In contrast, Konstantinov and Lampert (2021) showed for proper learners the loss in accuracy for both notions is $\Omega(1)$. The key technical novelty of our work is how randomization can bypass simple "tricks" an adversary can use to amplify his power. We also consider additional fairness notions including Equalized Odds and Calibration. For these fairness notions, the excess accuracy clusters into three natural regimes $O(\alpha)$,$O(\sqrt{\alpha})$ and $O(1)$. These results provide a more fine-grained view of the sensitivity of fairness-constrained learning to adversarial noise in training data.

In this paper, we propose a computationally efficient framework for interval reachability of neural network controlled systems. Our approach builds upon inclusion functions for the neural network controller and the open-loop system. We observe that many state-of-the-art neural network verifiers can produce inclusion functions for neural networks. We introduce and analyze a new class of inclusion functions for the open-loop dynamics based on bounds of the function Jacobian that is particularly suitable for capturing the interactions between systems and neural network controllers. Next, for any dynamical system, we use inclusion functions to construct an embedding system with twice the number of states as the original system. We show that a single trajectory of this embedding system provides hyper-rectangular over-approximations of reachable sets. We then propose two approaches for constructing a closed-loop embedding system for a neural network controlled dynamical system that accounts for the interaction between the system and the controller in different ways. The interconnection-based approach accounts for the worst-case evolution of each coordinate separately by substituting the neural network inclusion function into the open-loop embedding system. The interaction-based approach uses the newly introduced class of Jacobian-based inclusion functions to fully capture first-order interactions between the system and the controller. Finally, we implement our approach in a Python framework called \texttt{ReachMM} and show that on several existing benchmarks, our methods outperform the existing approaches in the literature. We also demonstrate the scalability of our method on a vehicle platooning example with up to $200$ states.

Nonconvex-nonconcave minimax optimization has gained widespread interest over the last decade. However, most existing works focus on variants of gradient descent-ascent (GDA) algorithms, which are only applicable to smooth nonconvex-concave settings. To address this limitation, we propose a novel algorithm named smoothed proximal linear descent-ascent (smoothed PLDA), which can effectively handle a broad range of structured nonsmooth nonconvex-nonconcave minimax problems. Specifically, we consider the setting where the primal function has a nonsmooth composite structure and the dual function possesses the Kurdyka-Lojasiewicz (KL) property with exponent $\theta \in [0,1)$. We introduce a novel convergence analysis framework for smoothed PLDA, the key components of which are our newly developed nonsmooth primal error bound and dual error bound. Using this framework, we show that smoothed PLDA can find both $\epsilon$-game-stationary points and $\epsilon$-optimization-stationary points of the problems of interest in $\mathcal{O}(\epsilon^{-2\max\{2\theta,1\}})$ iterations. Furthermore, when $\theta \in [0,\frac{1}{2}]$, smoothed PLDA achieves the optimal iteration complexity of $\mathcal{O}(\epsilon^{-2})$. To further demonstrate the effectiveness and wide applicability of our analysis framework, we show that certain max-structured problem possesses the KL property with exponent $\theta=0$ under mild assumptions. As a by-product, we establish algorithm-independent quantitative relationships among various stationarity concepts, which may be of independent interest.

Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to $66 \times$ while preserving the information required to perform segmentation with a dice coefficient of $0.84$ as compared to $0.88$ achieved using decompressed images while reducing the overall compute by $11\%$.

Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method, involves projecting onto basis functions represented as characteristic functions supported over a fine grid of rectangles. From this perspective, the Ulam-Galerkin approach can be interpreted as density estimation using the histogram method. In this study, we recast the problem within the framework of statistical density estimation. This alternative perspective allows for an explicit and rigorous analysis of bias and variance, thereby facilitating a discussion on the mean square error. Through comprehensive examples utilizing the logistic map and a Markov map, we demonstrate the validity and effectiveness of this approach in estimating the eigenvectors of the Frobenius-Perron operator. We compare the performance of Histogram Density Estimation(HDE) and Kernel Density Estimation(KDE) methods and find that KDE generally outperforms HDE in terms of accuracy. However, it is important to note that KDE exhibits limitations around boundary points and jumps. Based on our research findings, we suggest the possibility of incorporating other density estimation methods into this field and propose future investigations into the application of KDE-based estimation for high-dimensional maps. These findings provide valuable insights for researchers and practitioners working on estimating the Frobenius-Perron operator and highlight the potential of density estimation techniques in this area of study. Keywords: Transfer Operators; Frobenius-Perron operator; probability density estimation; Ulam-Galerkin method; Kernel Density Estimation; Histogram Density Estimation.

Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied outside their training domain, NN potential predictions can be inaccurate, increasing the need for Uncertainty Quantification (UQ). Bayesian modeling provides the mathematical framework for UQ, but classical Bayesian methods based on Markov chain Monte Carlo (MCMC) are computationally intractable for NN potentials. By training graph NN potentials for coarse-grained systems of liquid water and alanine dipeptide, we demonstrate here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables. We show that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed. Additionally, we find that SG-MCMC and the Deep Ensemble method achieve comparable results, despite shorter training and less hyperparameter tuning of the latter. We show that both methods can capture aleatoric and epistemic uncertainty reliably, but not systematic uncertainty, which needs to be minimized by adequate modeling to obtain accurate credible intervals for MD observables. Our results represent a step towards accurate UQ that is of vital importance for trustworthy NN potential-based MD simulations required for decision-making in practice.

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results.

Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system which leads to loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model Differential Fuzzy Convolutional Neural Network (DFCNN) utilizes a convolution neural network to re-implement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of non-stationary time series due to the trend of non-stationary time series. The trend of non-stationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the Difference algorithm to weaken the non-stationary of time series so that DFCNN can forecast the non-stationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.

The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit is the result of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.

Constraint programming is known for being an efficient approach for solving combinatorial problems. Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. To the best of our knowledge, it is still an open research question. Although several generic variable-selection heuristics are available in the literature, the options for a generic value-selection heuristic are more scarce. In this paper, we propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network architecture. Experiments on graph coloring, maximum independent set, and maximum cut problems show that our framework is able to find better solutions close to optimality without requiring a large amounts of backtracks while being generic.

Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.

This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.

In this work we propose an algorithm for trace recovery from stochastically known logs, a setting that is becoming more common with the increasing number of sensors and predictive models that generate uncertain data. The suggested approach calculates the conformance between a process model and a stochastically known trace and recovers the best alignment within this stochastic trace as the true trace. The paper offers an analysis of the impact of various cost models on trace recovery accuracy and makes use of a product multi-graph to compare alternative trace recovery options. The average accuracy of our approach, evaluated using two publicly available datasets, is impressive, with an average recovery accuracy score of 90-97%, significantly improving a common heuristic that chooses the most likely value for each uncertain activity. We believe that the effectiveness of the proposed algorithm in recovering correct traces from stochastically known logs may be a powerful aid for developing credible decision-making tools in uncertain settings.

Recognition problems in long-tailed data, where the sample size per class is heavily skewed, have recently gained importance because the distribution of the sample size per class in a dataset is generally exponential unless the sample size is intentionally adjusted. Various approaches have been devised to address these problems. Recently, weight balancing, which combines well-known classical regularization techniques with two-stage training, has been proposed. Despite its simplicity, it is known for its high performance against existing methods devised in various ways. However, there is a lack of understanding as to why this approach is effective for long-tailed data. In this study, we analyze the method focusing on neural collapse and cone effect at each training stage and find that it can be decomposed into the increase in Fisher's discriminant ratio of the feature extractor caused by weight decay and cross entropy loss and implicit logit adjustment caused by weight decay and class-balanced loss. Our analysis shows that the training method can be further simplified by reducing the number of training stages to one while increasing accuracy.

Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.

The Fourier neural operator (FNO) is a powerful technique for learning surrogate maps for partial differential equation (PDE) solution operators. For many real-world applications, which often require high-resolution data points, training time and memory usage are significant bottlenecks. While there are mixed-precision training techniques for standard neural networks, those work for real-valued datatypes on finite dimensions and therefore cannot be directly applied to FNO, which crucially operates in the (complex-valued) Fourier domain and in function spaces. On the other hand, since the Fourier transform is already an approximation (due to discretization error), we do not need to perform the operation at full precision. In this work, we (i) profile memory and runtime for FNO with full and mixed-precision training, (ii) conduct a study on the numerical stability of mixed-precision training of FNO, and (iii) devise a training routine which substantially decreases training time and memory usage (up to 34%), with little or no reduction in accuracy, on the Navier-Stokes and Darcy flow equations. Combined with the recently proposed tensorized FNO (Kossaifi et al., 2023), the resulting model has far better performance while also being significantly faster than the original FNO.

The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model's ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today's models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.

Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explainers are often relied on to provide interpretability to these black-box prediction models. As crucial diagnostics tools, it is important that these explainers themselves are robust. In this paper we focus on one particular aspect of robustness, namely that an explainer should give similar explanations for similar data inputs. We formalize this notion by introducing and defining explainer astuteness, analogous to astuteness of prediction functions. Our formalism allows us to connect explainer robustness to the predictor's probabilistic Lipschitzness, which captures the probability of local smoothness of a function. We provide lower bound guarantees on the astuteness of a variety of explainers (e.g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function. These theoretical results imply that locally smooth prediction functions lend themselves to locally robust explanations. We evaluate these results empirically on simulated as well as real datasets.

Solving parity games is a major building block for numerous applications in reactive program verification and synthesis. While they can be solved efficiently in practice, no known approach has a polynomial worst-case runtime complexity. We present a incomplete polynomial-time approach to determining the winning regions of parity games via graph neural networks. Our evaluation on 900 randomly generated parity games shows that this approach is effective and efficient in practice. It correctly determines the winning regions of $\sim$60\% of the games in our data set and only incurs minor errors in the remaining ones. We believe that this approach can be extended to efficiently solve parity games as well.

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data. To alleviate such limitations, we develop RetroPrompt with the motivation of decoupling knowledge from memorization to help the model strike a balance between generalization and memorization. In contrast with vanilla prompt learning, RetroPrompt constructs an open-book knowledge-store from training instances and implements a retrieval mechanism during the process of input, training and inference, thus equipping the model with the ability to retrieve related contexts from the training corpus as cues for enhancement. Extensive experiments demonstrate that RetroPrompt can obtain better performance in both few-shot and zero-shot settings. Besides, we further illustrate that our proposed RetroPrompt can yield better generalization abilities with new datasets. Detailed analysis of memorization indeed reveals RetroPrompt can reduce the reliance of language models on memorization; thus, improving generalization for downstream tasks. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt.

Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.

Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for feature extraction, and even differ on what they consider a malware family. As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other. This work sheds light on those open questions. by investigating the key factors influencing ML-based malware detection and classification. For this, we collect the largest balanced malware dataset so far with 67K samples from 670 families (100 samples each), and train state-of-the-art models for malware detection and family classification using our dataset. Our results reveal that static features perform better than dynamic features, and that combining both only provides marginal improvement over static features. We discover no correlation between packing and classification accuracy, and that missing behaviors in dynamically-extracted features highly penalize their performance. We also demonstrate how a larger number of families to classify make the classification harder, while a higher number of samples per family increases accuracy. Finally, we find that models trained on a uniform distribution of samples per family better generalize on unseen data.

We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.

Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations that preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations that mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms random sampling methods by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.

Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluation procedure, where the goal is to approximate the outcome of an online experiment. Several offline evaluation metrics have been adopted in the literature, inspired by ranking metrics prevalent in the field of Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is one such metric that has seen widespread adoption in empirical studies, and higher (n)DCG values have been used to present new methods as the state-of-the-art in top-$n$ recommendation for many years. Our work takes a critical look at this approach, and investigates when we can expect such metrics to approximate the gold standard outcome of an online experiment. We formally present the assumptions that are necessary to consider DCG an unbiased estimator of online reward and provide a derivation for this metric from first principles, highlighting where we deviate from its traditional uses in IR. Importantly, we show that normalising the metric renders it inconsistent, in that even when DCG is unbiased, ranking competing methods by their normalised DCG can invert their relative order. Through a correlation analysis between off- and on-line experiments conducted on a large-scale recommendation platform, we show that our unbiased DCG estimates strongly correlate with online reward, even when some of the metric's inherent assumptions are violated. This statement no longer holds for its normalised variant, suggesting that nDCG's practical utility may be limited.

It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity. We used artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization, and find that in general, this doesn't necessarily hold except at extreme levels. We then systematically tested which features of the environment and network do lead to the emergence of specialization. We used a simple toy environment, task and network, allowing us precise control, and show that in this setup, several distinct measures of specialization give qualitatively similar results. We further find that (1) specialization can only emerge in environments where features of that environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across different network architectures, but the quantitative relationships depends on the architecture type. Finally, we show that functional specialization varies dynamically across time, and demonstrate that these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization, based on structural modularity, is likely too simple a framework for understanding intelligent systems in situations of real-world complexity. We propose that thoroughly stress testing candidate definitions of functional modularity in simplified scenarios before extending to more complex data, network models and electrophysiological recordings is likely to be a fruitful approach.

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification, especially when the environment's dynamics are only partially known. This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state `task automata' from episodes of agent experience within unknown environments. We leverage two key algorithmic insights. First, we learn a product MDP, a model composed of the specification's automaton and the environment's MDP (both initially unknown), by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy. It also provides an interpretable encoding of high-level environmental and task features, so a human can readily verify that the agent has learnt coherent tasks with no misspecifications. In addition, we take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning. Finally, we provide experimental results compared with two baselines to illustrate our algorithm's performance in different environments and tasks.

Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequently, these factors contribute to reduced accuracy in digit recognition. To overcome this challenge, we propose a two-stage deep learning approach. In the first stage, we create a simple neural network to identify distorted digits within the training set. This model serves to detect and filter out such distorted and ambiguous images. In the second stage, we exclude these identified images from the training dataset and proceed to retrain the model using the filtered dataset. This process aims to improve the classification accuracy and confidence levels while mitigating issues of underfitting and overfitting. Our experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy rate of over 99.5% on the testing dataset. This significant improvement showcases the potential of our method in enhancing digit classification accuracy. In our future work, we intend to explore the scalability of this approach and investigate techniques to further enhance accuracy by reducing the size of the training data.

There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models. Consequently, there is a growing interest in detecting these shifts before associated costs have time to accumulate. However, desiderata of crucial importance to the practicable deployment of sequential shift detectors are typically overlooked by existing works, precluding their widespread adoption. We identify three such desiderata, highlight existing works relevant to their satisfaction, and recommend impactful directions for future research.

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based analytics tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 194 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics.

State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancement in stochastic thermodynamics which allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network, based on the ratio of their Wasserstein-2 distance and the entropy production rate of the dynamical process connecting them. Considering both gradient-flow and Langevin training dynamics, we provide analytical expressions for these speed limits for linear and linearizable neural networks e.g. Neural Tangent Kernel (NTK). Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense. Our results are consistent with small-scale experiments with Convolutional Neural Networks (CNNs) and Fully Connected Neural networks (FCNs) on CIFAR-10, showing a short highly non-optimal regime followed by a longer optimal regime.

As public consciousness regarding the collection and use of personal information by corporations grows, it is of increasing importance that consumers be active participants in the curation of corporate datasets. In light of this, data governance frameworks such as the General Data Protection Regulation (GDPR) have outlined the right to be forgotten as a key principle allowing individuals to request that their personal data be deleted from the databases and models used by organizations. To achieve forgetting in practice, several machine unlearning methods have been proposed to address the computational inefficiencies of retraining a model from scratch with each unlearning request. While efficient online alternatives to retraining, it is unclear how these methods impact other properties critical to real-world applications, such as fairness. In this work, we propose the first fair machine unlearning method that can provably and efficiently unlearn data instances while preserving group fairness. We derive theoretical results which demonstrate that our method can provably unlearn data instances while maintaining fairness objectives. Extensive experimentation with real-world datasets highlight the efficacy of our method at unlearning data instances while preserving fairness.

Handwriting recognition is a challenging and critical problem in the fields of pattern recognition and machine learning, with applications spanning a wide range of domains. In this paper, we focus on the specific issue of recognizing offline Arabic handwritten text. Existing approaches typically utilize a combination of convolutional neural networks for image feature extraction and recurrent neural networks for temporal modeling, with connectionist temporal classification used for text generation. However, these methods suffer from a lack of parallelization due to the sequential nature of recurrent neural networks. Furthermore, these models cannot account for linguistic rules, necessitating the use of an external language model in the post-processing stage to boost accuracy. To overcome these issues, we introduce two alternative architectures, namely the Transformer Transducer and the standard sequence-to-sequence Transformer, and compare their performance in terms of accuracy and speed. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. We employ pre-trained Transformers for both image understanding and language modeling. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches for recognizing offline Arabic handwritten text.

Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance

We present a new large-scale emotion-labeled symbolic music dataset consisting of 12k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.

In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of $O(\frac{1}{\sqrt{T}})$, where $T$ denotes the number of iteration. To the best of our knowledge, our new Compositional FL framework is the first work to bridge federated learning with composition stochastic optimization. In particular, we first transform the distributionally robust FL (i.e., a minimax optimization problem) into a simple composition optimization problem by using KL divergence regularization. At the same time, we also first transform the distribution-agnostic MAML problem (i.e., a minimax optimization problem) into a simple yet effective composition optimization problem. Finally, we apply two popular machine learning tasks, i.e., distributionally robust FL and MAML to demonstrate the effectiveness of our algorithm.

Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user profile establishment, intelligent household energy management, and peak load shifting. This is beneficial for both the end-users and utilities by improving the overall efficiency of a power distribution network. Existing approaches mainly focus on developing an individual model for each appliance. Those approaches typically rely on a large amount of household-labeled data which is hard to collect. In this paper, we propose a multi-appliance-task framework with a training-efficient sample augmentation (SA) scheme that boosts the disaggregation performance with limited labeled data. For each appliance, we develop a shared-hierarchical split structure for its regression and classification tasks. In addition, we also propose a two-dimensional attention mechanism in order to capture spatio-temporal correlations among all appliances. With only one-day training data and limited appliance operation profiles, the proposed SA algorithm can achieve comparable test performance to the case of training with the full dataset. Finally, simulation results show that our proposed approach features a significantly improved performance over many baseline models. The relative errors can be reduced by more than 50\% on average. The codes of this work are available at https://github.com/jxiong22/MATNilm

This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec+, TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SASRec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code at https://github.com/otto-de/TRON and an anonymized dataset at https://github.com/otto-de/recsys-dataset.

Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Markov decision process (POMDP) is a widely-used framework to model these stochastic and partially-observable decision-making problems. However, due to a lack of explicit causal semantics, POMDP planning methods are prone to confounding bias and thus in the presence of unobserved confounders may produce underperforming policies. This paper presents a novel causally-informed extension of "anytime regularized determinized sparse partially observable tree" (AR-DESPOT), a modern anytime online POMDP planner, using causal modelling and inference to eliminate errors caused by unmeasured confounder variables. We further propose a method to learn offline the partial parameterisation of the causal model for planning, from ground truth model data. We evaluate our methods on a toy problem with an unobserved confounder and show that the learned causal model is highly accurate, while our planning method is more robust to confounding and produces overall higher performing policies than AR-DESPOT.

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.

The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws that govern these systems. In this work, we propose a self-adaptive penalty algorithm for Neural ODEs to enable modelling of constrained natural systems. The proposed self-adaptive penalty function can dynamically adjust the penalty parameters. The explicit introduction of prior knowledge helps to increase the interpretability of Neural ODE -based models. We validate the proposed approach by modelling three natural systems with prior knowledge constraints: population growth, chemical reaction evolution, and damped harmonic oscillator motion. The numerical experiments and a comparison with other penalty Neural ODE approaches and \emph{vanilla} Neural ODE, demonstrate the effectiveness of the proposed self-adaptive penalty algorithm for Neural ODEs in modelling constrained natural systems. Moreover, the self-adaptive penalty approach provides more accurate and robust models with reliable and meaningful predictions.

This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.

Residual connections have been proposed as architecture-based inductive bias to mitigate the problem of exploding and vanishing gradients and increase task performance in both feed-forward and recurrent networks (RNNs) when trained with the backpropagation algorithm. Yet, little is known about how residual connections in RNNs influence their dynamics and fading memory properties. Here, we introduce weakly coupled residual recurrent networks (WCRNNs) in which residual connections result in well-defined Lyapunov exponents and allow for studying properties of fading memory. We investigate how the residual connections of WCRNNs influence their performance, network dynamics, and memory properties on a set of benchmark tasks. We show that several distinct forms of residual connections yield effective inductive biases that result in increased network expressivity. In particular, residual connections that (i) result in network dynamics at the proximity of the edge of chaos, (ii) allow networks to capitalize on characteristic spectral properties of the data, and (iii) result in heterogeneous memory properties are shown to increase practical expressivity. In addition, we demonstrate how our results can be extended to non-linear residuals and introduce a weakly coupled residual initialization scheme that can be used for Elman RNNs

Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc. In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark. Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.

Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing code representations can further enable human experts to gain an intuitive insight into the code. Unfortunately, as of today, there is no universal tool that can simultaneously visualise different types of code representations. In this paper, we introduce a tool, CodeLens, which provides a visual interaction environment that supports various representation methods and helps developers understand and explore them. CodeLens is designed to support multiple programming languages, such as Java, Python, and JavaScript, and four types of code representations, including sequence of tokens, abstract syntax tree (AST), data flow graph (DFG), and control flow graph (CFG). By using CodeLens, developers can quickly visualize the specific code representation and also obtain the represented inputs for models of code. The Web-based interface of CodeLens is available at this http URL The demonstration video can be found at this http URL

With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to explain models. Post hoc explanation methods explain the behaviour of complex black-box models by highlighting features that are critical to model predictions; however, prior work has shown that these explanations may not be faithful, and even more concerning is our inability to verify them. Specifically, it is nontrivial to evaluate if a given attribution is correct with respect to the underlying model. Inherently interpretable models, on the other hand, circumvent these issues by explicitly encoding explanations into model architecture, meaning their explanations are naturally faithful and verifiable, but they often exhibit poor predictive performance due to their limited expressive power. In this work, we aim to bridge the gap between the aforementioned strategies by proposing Verifiability Tuning (VerT), a method that transforms black-box models into models that naturally yield faithful and verifiable feature attributions. We begin by introducing a formal theoretical framework to understand verifiability and show that attributions produced by standard models cannot be verified. We then leverage this framework to propose a method to build verifiable models and feature attributions out of fully trained black-box models. Finally, we perform extensive experiments on semi-synthetic and real-world datasets, and show that VerT produces models that (1) yield explanations that are correct and verifiable and (2) are faithful to the original black-box models they are meant to explain.

Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important tool for practical machine learning is the model Exponential Moving Average (EMA), which is a model copy that does not receive gradient information, but instead follows its target model with some momentum. This model EMA can improve the robustness and generalization properties of supervised learning, stabilize pseudo-labeling, and provide a learning signal for Self-Supervised Learning (SSL). Prior works have treated the model EMA separately from optimization, leading to different training dynamics across batch sizes and lower model performance. In this work, we provide a scaling rule for optimization in the presence of model EMAs and demonstrate its validity across a range of architectures, optimizers, and data modalities. We also show the rule's validity where the model EMA contributes to the optimization of the target model, enabling us to train EMA-based pseudo-labeling and SSL methods at small and large batch sizes. For SSL, we enable training of BYOL up to batch size 24,576 without sacrificing performance, optimally a 6$\times$ wall-clock time reduction.

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Our first contribution is to propose a simple primitive that allows for efficient implementation of several commonly used algorithms, and allows for privacy accounting that is close to that in the central setting without requiring the strong trust assumptions it entails. Second, we propose a system architecture that implements this primitive and perform a security analysis of the proposed system.

The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. To overcome these challenges, we propose a 2D medical image segmentation model called Multi-scale Cross Perceptron Attention Network (MCPA). The MCPA consists of three main components: an encoder, a decoder, and a Cross Perceptron. The Cross Perceptron first captures the local correlations using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of features across scales. The resulting multi-scale feature vectors are then spatially unfolded, concatenated, and fed through a Global Perceptron module to model global dependencies. Furthermore, we introduce a Progressive Dual-branch Structure to address the semantic segmentation of the image involving finer tissue structures. This structure gradually shifts the segmentation focus of MCPA network training from large-scale structural features to more sophisticated pixel-level features. We evaluate our proposed MCPA model on several publicly available medical image datasets from different tasks and devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC), fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental results show that our MCPA model achieves state-of-the-art performance. The code is available at https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.

Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling strategies can only capture a small amount of intrinsic supervision information. Additionally, the existing methods require complex designs to obtain two different representations of the data. To overcome these limitations, we propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two main components: the Attentional Module (AttM) and the Diffusion Module (DiFM). AttM aggregates higher-order structure and feature information to get an excellent embedding, while DiFM balances the state of each node in the graph through Laplacian diffusion learning and allows the cooperative evolution of adjacency and feature information in the graph. Unlike existing methodologies, SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic drift, without the need for pre-training. We conduct a high-quality sampling of samples based on structure and feature information. If two nodes are neighbors, they are considered positive samples of each other. If two disconnected nodes are also unrelated on $k$NN graph, they are considered negative samples for each other. The contrastive objective reasonably uses our proposed sampling strategies, and the redundancy reduction term minimizes redundant information in the embedding and can well retain more discriminative information. In this novel framework, the graph self-contrastive learning paradigm gives expression to a powerful force. SCGDN effectively balances between preserving high-order structure information and avoiding overfitting. The results manifest that SCGDN can consistently generate outperformance over both the contrastive methods and the classical methods.

Here we develop variants of SGD (stochastic gradient descent) with an adaptive step size that make use of the sampled loss values. In particular, we focus on solving a finite sum-of-terms problem, also known as empirical risk minimization. We first detail an idealized adaptive method called $\texttt{SPS}_+$ that makes use of the sampled loss values and assumes knowledge of the sampled loss at optimality. This $\texttt{SPS}_+$ is a minor modification of the SPS (Stochastic Polyak Stepsize) method, where the step size is enforced to be positive. We then show that $\texttt{SPS}_+$ achieves the best known rates of convergence for SGD in the Lipschitz non-smooth. We then move onto to develop $\texttt{FUVAL}$, a variant of $\texttt{SPS}_+$ where the loss values at optimality are gradually learned, as opposed to being given. We give three viewpoints of $\texttt{FUVAL}$, as a projection based method, as a variant of the prox-linear method, and then as a particular online SGD method. We then present a convergence analysis of $\texttt{FUVAL}$ and experimental results. The shortcomings of our work is that the convergence analysis of $\texttt{FUVAL}$ shows no advantage over SGD. Another shortcomming is that currently only the full batch version of $\texttt{FUVAL}$ shows a minor advantages of GD (Gradient Descent) in terms of sensitivity to the step size. The stochastic version shows no clear advantage over SGD. We conjecture that large mini-batches are required to make $\texttt{FUVAL}$ competitive. Currently the new $\texttt{FUVAL}$ method studied in this paper does not offer any clear theoretical or practical advantage. We have chosen to make this draft available online nonetheless because of some of the analysis techniques we use, such as the non-smooth analysis of $\texttt{SPS}_+$, and also to show an apparently interesting approach that currently does not work.

Large language models (LLMs) are now highly capable at a diverse range of tasks. This paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in the field of adversarial machine learning. As a case study, we evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023, a top computer security conference. We completely break this defense: the proposed scheme does not increase robustness compared to an undefended baseline. We write none of the code to attack this model, and instead prompt GPT-4 to implement all attack algorithms following our instructions and guidance. This process was surprisingly effective and efficient, with the language model at times producing code from ambiguous instructions faster than the author of this paper could have done. We conclude by discussing (1) the warning signs present in the evaluation that suggested to us AI-Guardian would be broken, and (2) our experience with designing attacks and performing novel research using the most recent advances in language modeling.

Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component operating based on ML. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus maintenance and testing efforts, by giving insights into the most error-prone components, most common bugs, etc. In this paper, we investigate the characteristics of bugs in ML-based software systems and the difference between ML and non-ML bugs from the maintenance viewpoint. We extracted 447,948 GitHub repositories that used one of the three most popular ML frameworks, i.e., TensorFlow, Keras, and PyTorch. After multiple filtering steps, we select the top 300 repositories with the highest number of closed issues. We manually investigate the extracted repositories to exclude non-ML-based systems. Our investigation involved a manual inspection of 386 sampled reported issues in the identified ML-based systems to indicate whether they affect ML components or not. Our analysis shows that nearly half of the real issues reported in ML-based systems are ML bugs, indicating that ML components are more error-prone than non-ML components. Next, we thoroughly examined 109 identified ML bugs to identify their root causes, symptoms, and calculate their required fixing time. The results also revealed that ML bugs have significantly different characteristics compared to non-ML bugs, in terms of the complexity of bug-fixing (number of commits, changed files, and changed lines of code). Based on our results, fixing ML bugs are more costly and ML components are more error-prone, compared to non-ML bugs and non-ML components respectively. Hence, paying a significant attention to the reliability of the ML components is crucial in ML-based systems.

How to accurately measure the relevance and redundancy of features is an age-old challenge in the field of feature selection. However, existing filter-based feature selection methods cannot directly measure redundancy for continuous data. In addition, most methods rely on manually specifying the number of features, which may introduce errors in the absence of expert knowledge. In this paper, we propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy, abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel density estimation on the features to capture their similarities and differences in inter-class and overall distributions. Subsequently, we present the criteria for maximum inter-class variation and minimum redundancy (MVMR), wherein the inter-class probability distributions are employed to reflect feature relevance and the distances between overall probability distributions are used to quantify redundancy. Finally, we employ an AGA to search for the feature subset that minimizes the MVMR. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%.

Armoured vehicles are specialized and complex pieces of machinery designed to operate in high-stress environments, often in combat or tactical situations. This study proposes a predictive maintenance-based ensemble system that aids in predicting potential maintenance needs based on sensor data collected from these vehicles. The proposed model's architecture involves various models such as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier and Gradient Boosting to predict the maintenance requirements of the vehicles accurately. In addition, K-fold cross validation, along with TOPSIS analysis, is employed to evaluate the proposed ensemble model's stability. The results indicate that the proposed system achieves an accuracy of 98.93%, precision of 99.80% and recall of 99.03%. The algorithm can effectively predict maintenance needs, thereby reducing vehicle downtime and improving operational efficiency. Through comparisons between various algorithms and the suggested ensemble, this study highlights the potential of machine learning-based predictive maintenance solutions.

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.

Counterfactual examples have emerged as an effective approach to produce simple and understandable post-hoc explanations. In the context of graph classification, previous work has focused on generating counterfactual explanations by manipulating the most elementary units of a graph, i.e., removing an existing edge, or adding a non-existing one. In this paper, we claim that such language of explanation might be too fine-grained, and turn our attention to some of the main characterizing features of real-world complex networks, such as the tendency to close triangles, the existence of recurring motifs, and the organization into dense modules. We thus define a general density-based counterfactual search framework to generate instance-level counterfactual explanations for graph classifiers, which can be instantiated with different notions of dense substructures. In particular, we show two specific instantiations of this general framework: a method that searches for counterfactual graphs by opening or closing triangles, and a method driven by maximal cliques. We also discuss how the general method can be instantiated to exploit any other notion of dense substructures, including, for instance, a given taxonomy of nodes. We evaluate the effectiveness of our approaches in 7 brain network datasets and compare the counterfactual statements generated according to several widely-used metrics. Results confirm that adopting a semantic-relevant unit of change like density is essential to define versatile and interpretable counterfactual explanation methods.

We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator and provides explicit non-asymptotic complexity guarantees for both.

Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongside text prompts during conversations. Given Bard's impressive track record in handling textual inputs, we explore its capabilities in understanding and interpreting visual data (images) conditioned by text questions. This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models, especially in addressing complex computer vision problems that demand accurate visual and language understanding. Specifically, in this study, we focus on 15 diverse task scenarios encompassing regular, camouflaged, medical, under-water and remote sensing data to comprehensively evaluate Bard's performance. Our primary finding indicates that Bard still struggles in these vision scenarios, highlighting the significant gap in vision-based understanding that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, leading to enhanced capabilities in comprehending and interpreting fine-grained visual data. Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand

Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players' actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.

Turbulence parametrizations will remain a necessary building block in kilometer-scale Earth system models. In convective boundary layers, where the mean vertical gradients of conserved properties such as potential temperature and moisture are approximately zero, the standard ansatz which relates turbulent fluxes to mean vertical gradients via an eddy diffusivity has to be extended by mass flux parametrizations for the typically asymmetric up- and downdrafts in the atmospheric boundary layer. In this work, we present a parametrization for a dry convective boundary layer based on a generative adversarial network. The model incorporates the physics of self-similar layer growth following from the classical mixed layer theory by Deardorff. This enhances the training data base of the generative machine learning algorithm and thus significantly improves the predicted statistics of the synthetically generated turbulence fields at different heights inside the boundary layer. The algorithm training is based on fully three-dimensional direct numerical simulation data. Differently to stochastic parametrizations, our model is able to predict the highly non-Gaussian transient statistics of buoyancy fluctuations, vertical velocity, and buoyancy flux at different heights thus also capturing the fastest thermals penetrating into the stabilized top region. The results of our generative algorithm agree with standard two-equation or multi-plume stochastic mass-flux schemes. The present parametrization provides additionally the granule-type horizontal organization of the turbulent convection which cannot be obtained in any of the other model closures. Our work paves the way to efficient data-driven convective parametrizations in other natural flows, such as moist convection, upper ocean mixing, or convection in stellar interiors.

We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform planning by proposing alternative plans to the world model before selecting a final action to execute in the environment. This approach eliminates the need for hand-crafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization. We demonstrate the algorithm's effectiveness through experimental results in the game of Sokoban and the Atari 2600 benchmark, where the Thinker algorithm achieves state-of-the-art performance and competitive results, respectively. Visualizations of agents trained with the Thinker algorithm demonstrate that they have learned to plan effectively with the world model to select better actions. The algorithm's generality opens a new research direction on how a world model can be used in reinforcement learning and how planning can be seamlessly integrated into an agent's decision-making process.

In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multi-physics phenomena. To bridge this gap, we present the BubbleML Dataset(https://github.com/HPCForge/BubbleML) which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 51 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multi-physics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.

Performance Benchmarking of HPC systems is an ongoing effort that seeks to provide information that will allow for increased performance and improve the job schedulers that manage these systems. We develop a benchmarking tool that utilizes machine learning models and gathers performance data on GPU-accelerated nodes while they perform material segmentation analysis. The benchmark uses a ML model that has been converted from Caffe to PyTorch using the MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered on two ERDC DSRC systems, Onyx and Vulcanite. The data reveals that while Vulcanite has faster model times in a large number of benchmarks, and it is also more subject to some environmental factors that can cause performances slower than Onyx. In contrast the model times from Onyx are consistent across benchmarks.

Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships. Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times. This opens the door to a new form of human-computer interaction in the context of music co-creation. After training the model on existing MIDI datasets, the experiments show that the model is able to generate appealing short and long musical sequences and to realistically interpolate between them, producing music that is tonally and rhythmically consistent. Finally, the visualization of the embeddings shows that the model is able to organize its latent space in accordance with known musical concepts.

In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at \url{https://github.com/xmed-lab/Fed-MAS}.

Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices. We apply these ideas to Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs state sampling are the main computational bottleneck during training. By using a coreset in place of the full data set, we try to minimize the number of steps needed and accelerate the overall training time. In a regime where computational time on quantum computers is a precious resource, we propose this might lead to substantial practical savings. We evaluate this approach on 6x6 binary images from an augmented bars and stripes data set using a QBM with 36 visible units and 8 hidden units. Using an Inception score inspired metric, we compare QBM training times with and without using coresets.

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global sensitivity analysis method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation.

The ever-growing use of wind energy makes necessary the optimization of turbine operations through pitch angle controllers and their maintenance with early fault detection. It is crucial to have accurate and robust models imitating the behavior of wind turbines, especially to predict the generated power as a function of the wind speed. Existing empirical and physics-based models have limitations in capturing the complex relations between the input variables and the power, aggravated by wind variability. Data-driven methods offer new opportunities to enhance wind turbine modeling of large datasets by improving accuracy and efficiency. In this study, we used physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm, while imposing certain physical constraints to the model. The developed models for regression of the power, torque, and power coefficient as output variables showed great accuracy for both real data and physical equations governing the system. Lastly, introducing an efficient evidential layer provided uncertainty estimations of the predictions, proved to be consistent with the absolute error, and made possible the definition of a confidence interval in the power curve.

Supervised classification algorithms are used to solve a growing number of real-life problems around the globe. Their performance is strictly connected with the quality of labels used in training. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice. To tackle this challenge, active learning algorithms are commonly employed to select only the most relevant data for labeling. However, this is possible only when the quality and quantity of labels acquired from experts are sufficient. Unfortunately, in many applications, a trade-off between annotating individual samples by multiple annotators to increase label quality vs. annotating new samples to increase the total number of labeled instances is necessary. In this paper, we address the issue of faulty data annotations in the context of active learning. In particular, we propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space. The proposed methods require little to no intersection between samples annotated by different experts. Our experiments on four public datasets indicate the robustness and superiority of the proposed methods in both, the estimation of the annotator's reliability, and the assignment of actual labels, against the state-of-the-art algorithms and the simple majority voting.

Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation. Recent proposals on Spatial-Temporal Graph Neural Networks (STGNNs) have made significant progress by combining sequential models with graph convolution networks. However, due to high complexity issues, STGNNs only focus on short-term traffic forecasting, e.g., 1-hour forecasting, while ignoring more practical long-term forecasting. In this paper, we make the first attempt to explore long-term traffic forecasting, e.g., 1-day forecasting. To this end, we first reveal its unique challenges in exploiting multi-scale representations. Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting. HUTFormer consists of a hierarchical encoder and decoder to jointly generate and utilize multi-scale representations of traffic data. Specifically, for the encoder, we propose window self-attention and segment merging to extract multi-scale representations from long-term traffic data. For the decoder, we design a cross-scale attention mechanism to effectively incorporate multi-scale representations. In addition, HUTFormer employs an efficient input embedding strategy to address the complexity issues. Extensive experiments on four traffic datasets show that the proposed HUTFormer significantly outperforms state-of-the-art traffic forecasting and long time series forecasting baselines.

AB testing aids business operators with their decision making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there is usually a gap between the requirements of practitioners, and the constraints imposed by the statistical hypothesis testing methodologies commonly used for analysis of AB tests. These include the lack of statistical power in multivariate designs with many factors, correlations between these factors, the need of sequential testing for early stopping, and the inability to pool knowledge from past tests. Here, we propose a solution that applies hierarchical Bayesian estimation to address the above limitations. In comparison to current sequential AB testing methodology, we increase statistical power by exploiting correlations between factors, enabling sequential testing and progressive early stopping, without incurring excessive false positive risk. We also demonstrate how this methodology can be extended to enable the extraction of composite global learnings from past AB tests, to accelerate future tests. We underpin our work with a solid theoretical framework that articulates the value of hierarchical estimation. We demonstrate its utility using both numerical simulations and a large set of real-world AB tests. Together, these results highlight the practical value of our approach for statistical inference in the technology industry.

Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the instances come from either the positive or negative class and the goal is to learn a ranking function that ranks positive instances higher than negative ones. While there could be a trade-off between fairness and performance, we propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking and maintaining the algorithm classification performance. In particular, we optimize a weighted sum of the utility as identifying an optimal warping path across different protected groups and solve it through a dynamic programming process. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. In addition to binary groups, xOrder can be applied to multiple protected groups. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories. xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics. From the visualization of the calibrated ranking scores, xOrder mitigates the score distribution shifts of different groups compared with baselines. Moreover, additional analytical results verify that xOrder achieves a robust performance when faced with fewer samples and a bigger difference between training and testing ranking score distributions.

Recent approaches in source separation leverage semantic information about their input mixtures and constituent sources that when used in conditional separation models can achieve impressive performance. Most approaches along these lines have focused on simple descriptions, which are not always useful for varying types of input mixtures. In this work, we present an approach in which a model, given an input mixture and partial semantic information about a target source, is trained to extract additional semantic data. We then leverage this pre-trained model to improve the separation performance of an uncoupled multi-conditional separation network. Our experiments demonstrate that the separation performance of this multi-conditional model is significantly improved, approaching the performance of an oracle model with complete semantic information. Furthermore, our approach achieves performance levels that are comparable to those of the best performing specialized single conditional models, thus providing an easier to use alternative.

To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. The open-source benchmark and tool are at: https://github.com/IML-DKFZ/sf-visuals.

The complex nature of big biological systems pushed some scientists to classify its understanding under the inconceivable missions. Different leveled challenges complicated this task, one of is the prediction of a protein's function. In recent years, significant progress has been made in this field through the development of various machine learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e assigning predefined labels to proteins. In this work, we propose a novel approach, \textbf{Prot2Text}, which predicts a protein function's in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including proteins' sequences, structures, and textual annotations. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate prediction of proteins' functions. The code, the models and a demo will be publicly released.

Federated learning (FL) is the most popular distributed machine learning technique. However, implementation of FL over modern wireless networks faces key challenges caused by (i) dynamics of the network conditions and (ii) the coexistence of multiple FL services/tasks and other network services in the system, which are not jointly considered in prior works. Motivated by these challenges, we introduce a generic FL paradigm over NextG networks, called dynamic multi-service FL (DMS-FL). We identify three unexplored design considerations in DMS-FL: (i) FL service operator accumulation, (ii) wireless resource fragmentation, and (iii) signal strength fluctuations. We take the first steps towards addressing these design considerations by proposing a novel distributed ML architecture called elastic virtualized FL (EV-FL). EV-FL unleashes the full potential of Open RAN (O-RAN) systems and introduces an elastic resource provisioning methodology to execute FL services. It further constitutes a multi-time-scale FL management system that introduces three dimensions into existing FL architectures: (i) virtualization, (ii) scalability, and (iii) elasticity. Through investigating EV-FL, we reveal a series of open research directions for future work. We finally simulate EV-FL to demonstrate its potential in saving wireless resources and increasing fairness among FL services.

This study introduces and empirically tests a novel predictive model for digital information engagement (IE) - the READ model, an acronym for the four pivotal attributes of engaging information: Representativeness, Ease-of-use, Affect, and Distribution. Conceptualized within the theoretical framework of Cumulative Prospect Theory, the model integrates key cognitive biases with computational linguistics and natural language processing to develop a multidimensional perspective on information engagement. A rigorous testing protocol was implemented, involving 50 randomly selected pairs of synonymous words (100 words in total) from the WordNet database. These words' engagement levels were evaluated through a large-scale online survey (n = 80,500) to derive empirical IE metrics. The READ attributes for each word were then computed and their predictive efficacy examined. The findings affirm the READ model's robustness, accurately predicting a word's IE level and distinguishing the more engaging word from a pair of synonyms with an 84% accuracy rate. The READ model's potential extends across various domains, including business, education, government, and healthcare, where it could enhance content engagement and inform AI language model development and generative text work. Future research should address the model's scalability and adaptability across different domains and languages, thereby broadening its applicability and efficacy.

Wide neural networks are biased towards learning certain functions, influencing both the rate of convergence of gradient descent (GD) and the functions that are reachable with GD in finite training time. As such, there is a great need for methods that can modify this bias according to the task at hand. To that end, we introduce Modified Spectrum Kernels (MSKs), a novel family of constructed kernels that can be used to approximate kernels with desired eigenvalues for which no closed form is known. We leverage the duality between wide neural networks and Neural Tangent Kernels and propose a preconditioned gradient descent method, which alters the trajectory of GD. As a result, this allows for a polynomial and, in some cases, exponential training speedup without changing the final solution. Our method is both computationally efficient and simple to implement.

In this paper, we study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis. Considering the wavelet transform represents the image in spatial and frequency domains, we carefully design a novel architecture SFUNet to effectively capture the correlation for both domains. Specifically, in the standard denoising U-Net for pixel data, we supplement the 2D convolutions and spatial-only attention layers with our spatial frequency-aware convolution and attention modules to jointly model the complementary information from spatial and frequency domains in wavelet data. Our new architecture can be used as a drop-in replacement to the pixel-based network and is compatible with the vanilla DDPM training process. By explicitly modeling the wavelet signals, we find our model is able to generate images with higher quality on CIFAR-10, FFHQ, LSUN-Bedroom, and LSUN-Church datasets, than the pixel-based counterpart.

Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients. Secondly, HPL in each client captures the hierarchical information in local data with the supervision of shared class prototypes in the hyperbolic model space. Additionally, CA in the server mitigates the impact of the inconsistent deviations from clients to server. Extensive studies of four datasets prove that HyperFed is effective in enhancing the performance of FL under the non-IID set.

With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The utility of such an examiner is demonstrated for verifying automatically generated reports by detecting and removing fake sentences. Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.

Visual AI systems are vulnerable to natural and synthetic physical corruption in the real-world. Such corruption often arises unexpectedly and alters the model's performance. In recent years, the primary focus has been on adversarial attacks. However, natural corruptions (e.g., snow, fog, dust) are an omnipresent threat to visual AI systems and should be considered equally important. Many existing works propose interesting solutions to train robust models against natural corruption. These works either leverage image augmentations, which come with the additional cost of model training, or place suspicious patches in the scene to design unadversarial examples. In this work, we propose the idea of naturalistic support artifacts (NSA) for robust prediction. The NSAs are shown to be beneficial in scenarios where model parameters are inaccessible and adding artifacts in the scene is feasible. The NSAs are natural looking objects generated through artifact training using DC-GAN to have high visual fidelity in the scene. We test against natural corruptions on the Imagenette dataset and observe the improvement in prediction confidence score by four times. We also demonstrate NSA's capability to increase adversarial accuracy by 8\% on average. Lastly, we qualitatively analyze NSAs using saliency maps to understand how they help improve prediction confidence.

With the development of Big data technology, data analysis has become increasingly important. Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex datasets. In this paper, we address these problems by proposing a data-driven Bregman divergence parameter optimization clustering algorithm (DBGSA), which combines the Universal Gravitational Algorithm to bring similar points closer in the dataset. We construct a gravitational coefficient equation with a special property that gradually reduces the influence factor as the iteration progresses. Furthermore, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8\% compared to other similar approaches like enhanced clustering algorithms and improved datasets. Additionally, a three-dimensional grid search was established to compare the effects of different parameter values within threshold conditions, and it was discovered the parameter set provided by our model is optimal. This finding provides strong evidence of the high accuracy and robustness of the algorithm.

This study presents a machine learning model based on the Naive Bayes classifier for predicting the level of depression in university students, the objective was to improve prediction accuracy using a machine learning model involving 70% training data and 30% validation data based on the Naive Bayes classifier, the collected data includes factors associated with depression from 519 university students, the results showed an accuracy of 78.03%, high sensitivity in detecting positive cases of depression, especially at moderate and severe levels, and significant specificity in correctly classifying negative cases, these findings highlight the effectiveness of the model in early detection and treatment of depression, benefiting vulnerable sectors and contributing to the improvement of mental health in the student population.

In this work, we bound a machine's ability to learn based on computational limitations implied by physicality. We start by considering the information processing capacity (IPC), a normalized measure of the expected squared error of a collection of signals to a complete basis of functions. We use the IPC to measure the degradation under noise of the performance of reservoir computers, a particular kind of recurrent network, when constrained by physical considerations. First, we show that the IPC is at most a polynomial in the system size $n$, even when considering the collection of $2^n$ possible pointwise products of the $n$ output signals. Next, we argue that this degradation implies that the family of functions represented by the reservoir requires an exponential number of samples to learn in the presence of the reservoir's noise. Finally, we conclude with a discussion of the performance of the same collection of $2^n$ functions without noise when being used for binary classification.

Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a point cloud. In a grid, on the other hand, we can compute the kernel only once and reuse it for all query positions. As a result, operations that rely on neighborhood information scale much worse for point clouds than for grid data, specially for large inputs and large neighborhoods. In this work, we address the scalability issue of point cloud methods by tackling its root cause: the irregularity of the data. We propose learnable gridification as the first step in a point cloud processing pipeline to transform the point cloud into a compact, regular grid. Thanks to gridification, subsequent layers can use operations defined on regular grids, e.g., Conv3D, which scale much better than native point cloud methods. We then extend gridification to point cloud to point cloud tasks, e.g., segmentation, by adding a learnable de-gridification step at the end of the point cloud processing pipeline to map the compact, regular grid back to its original point cloud form. Through theoretical and empirical analysis, we show that gridified networks scale better in terms of memory and time than networks directly applied on raw point cloud data, while being able to achieve competitive results. Our code is publicly available at https://github.com/computri/gridifier.

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, we propose a REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.

This paper analyzes representations of continuous piecewise linear functions with infinite width, finite cost shallow neural networks using the rectified linear unit (ReLU) as an activation function. Through its integral representation, a shallow neural network can be identified by the corresponding signed, finite measure on an appropriate parameter space. We map these measures on the parameter space to measures on the projective $n$-sphere cross $\mathbb{R}$, allowing points in the parameter space to be bijectively mapped to hyperplanes in the domain of the function. We prove a conjecture of Ongie et al. that every continuous piecewise linear function expressible with this kind of infinite width neural network is expressible as a finite width shallow ReLU neural network.

Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well. However, CNNs require large amounts of training data, which is potentially problematic in the domain of expensive cosmological simulations, and it is difficult to interpret the network. In this work we apply the learnable scattering transform, a kind of convolutional neural network that uses trainable wavelets as filters, to the problem of cosmological inference and marginalisation over astrophysical effects. We present two models based on the scattering transform, one constructed for performance, and one constructed for interpretability, and perform a comparison with a CNN. We find that scattering architectures are able to outperform a CNN, significantly in the case of small training data samples. Additionally we present a lightweight scattering network that is highly interpretable.

This study provides a comprehensive time series analysis of daily industry-specific, country-wise CO$_2$ emissions from January 2019 to February 2023. The research focuses on the Power, Industry, Ground Transport, Domestic Aviation, and International Aviation sectors in European countries (EU27 & UK, Italy, Germany, Spain) and India, utilizing near-real-time activity data from the Carbon Monitor research initiative. To identify regular emission patterns, the data from the year 2020 is excluded due to the disruptive effects caused by the COVID-19 pandemic. The study then performs a principal component analysis (PCA) to determine the key contributors to CO$_2$ emissions. The analysis reveals that the Power, Industry, and Ground Transport sectors account for a significant portion of the variance in the dataset. A 7-day moving averaged dataset is employed for further analysis to facilitate robust predictions. This dataset captures both short-term and long-term trends and enhances the quality of the data for prediction purposes. The study utilizes Long Short-Term Memory (LSTM) models on the 7-day moving averaged dataset to effectively predict emissions and provide insights for policy decisions, mitigation strategies, and climate change efforts. During the training phase, the stability and convergence of the LSTM models are ensured, which guarantees their reliability in the testing phase. The evaluation of the loss function indicates this reliability. The model achieves high efficiency, as demonstrated by $R^2$ values ranging from 0.8242 to 0.995 for various countries and sectors. Furthermore, there is a proposal for utilizing scandium and boron/aluminium-based thin films as exceptionally efficient materials for capturing CO$_2$ (with a binding energy range from -3.0 to -3.5 eV). These materials are shown to surpass the affinity of graphene and boron nitride sheets in this regard.

Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating a continuous representation of nodes in a graph. A continuous representation is often more amenable, especially at scale, for solving downstream machine learning tasks such as classification, link prediction, and clustering. A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. We verify the embedding space quality on real and synthetic large-scale datasets.

Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention. However, most existing methods adopt pure data-driven black-box models, which have limited accuracy and interpretability. By combining trainable difference operators with black-box models, we propose a new hybrid architecture explicitly embedded with partial prior knowledge of the underlying PDEs named PDE-Net++. Furthermore, we introduce two distinct options called the trainable flipping difference layer (TFDL) and the trainable dynamic difference layer (TDDL) for the difference operators. Numerous numerical experiments have demonstrated that PDE-Net++ has superior prediction accuracy and better extrapolation performance than black-box models.

Recent work in the field of speech enhancement (SE) has involved the use of self-supervised speech representations (SSSRs) as feature transformations in loss functions. However, in prior work, very little attention has been paid to the relationship between the language of the audio used to train the self-supervised representation and that used to train the SE system. Enhancement models trained using a loss function which incorporates a self-supervised representation that shares exactly the language of the noisy data used to train the SE system show better performance than those which do not match exactly. This may lead to enhancement systems which are language specific and as such do not generalise well to unseen languages, unlike models trained using traditional spectrogram or time domain loss functions. In this work, SE models are trained and tested on a number of different languages, with self-supervised representations which themselves are trained using different language combinations and with differing network structures as loss function representations. These models are then tested across unseen languages and their performances are analysed. It is found that the training language of the self-supervised representation appears to have a minor effect on enhancement performance, the amount of training data of a particular language, however, greatly affects performance.

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the federated distributionally robust optimization (FDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kinds of contracts help API users catch errors at earlier stages in the ML pipeline. We describe an empirical study of posts on Stack Overflow of the four most often-discussed ML libraries: TensorFlow, Scikit-learn, Keras, and PyTorch. For these libraries, our study extracted 413 informal (English) API specifications. We used these specifications to understand the following questions. What are the root causes and effects behind ML contract violations? Are there common patterns of ML contract violations? When does understanding ML contracts require an advanced level of ML software expertise? Could checking contracts at the API level help detect the violations in early ML pipeline stages? Our key findings are that the most commonly needed contracts for ML APIs are either checking constraints on single arguments of an API or on the order of API calls. The software engineering community could employ existing contract mining approaches to mine these contracts to promote an increased understanding of ML APIs. We also noted a need to combine behavioral and temporal contract mining approaches. We report on categories of required ML contracts, which may help designers of contract languages.

Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions. Several regularization approaches that exploit spatial-temporal redundancy have been proposed to reconstruct undersampled cardiac cine MRI. More recently, methods based on supervised deep learning have been also proposed to further accelerate acquisition and reconstruction. However, these techniques rely on usually large dataset for training, which are not always available. In this work, we propose an unsupervised approach based on implicit neural field representations for cardiac cine MRI (so called NF-cMRI). The proposed method was evaluated in in-vivo undersampled golden-angle radial multi-coil acquisitions for undersampling factors of 26x and 52x, achieving good image quality, and comparable spatial and improved temporal depiction than a state-of-the-art reconstruction technique.

Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider Mixed-Membership Stochastic Block Model (MMSB) first proposed by Airoldi et al. (2008). MMSB provides quite a general setting for modeling overlapping community structure in graphs. The central question of this paper is to reconstruct relations between communities given an observed network. We compare different approaches and establish the minimax lower bound on the estimation error. Then, we propose a new estimator that matches this lower bound. Theoretical results are proved under fairly general conditions on the considered model. Finally, we illustrate the theory in a series of experiments.

In machine learning, generative modeling aims to learn to generate new data statistically similar to the training data distribution. In this paper, we survey learning generative models under limited data, few shots and zero shot, referred to as Generative Modeling under Data Constraint (GM-DC). This is an important topic when data acquisition is challenging, e.g. healthcare applications. We discuss background, challenges, and propose two taxonomies: one on GM-DC tasks and another on GM-DC approaches. Importantly, we study interactions between different GM-DC tasks and approaches. Furthermore, we highlight research gaps, research trends, and potential avenues for future exploration. Project website: https://gmdc-survey.github.io.

The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimensional representation and construct an NN that outputs this representation. Then, we propose to modify the output of an NN via the objective function and introduce different objective functions for network training. The proposed models are suited for both regularly and irregularly spaced data, and a roughness penalty can be further applied to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.

In this work, we explore a framework for contextual decision-making to study how the relevance and quantity of past data affects the performance of a data-driven policy. We analyze a contextual Newsvendor problem in which a decision-maker needs to trade-off between an underage and an overage cost in the face of uncertain demand. We consider a setting in which past demands observed under ``close by'' contexts come from close by distributions and analyze the performance of data-driven algorithms through a notion of context-dependent worst-case expected regret. We analyze the broad class of Weighted Empirical Risk Minimization (WERM) policies which weigh past data according to their similarity in the contextual space. This class includes classical policies such as ERM, k-Nearest Neighbors and kernel-based policies. Our main methodological contribution is to characterize exactly the worst-case regret of any WERM policy on any given configuration of contexts. To the best of our knowledge, this provides the first understanding of tight performance guarantees in any contextual decision-making problem, with past literature focusing on upper bounds via concentration inequalities. We instead take an optimization approach, and isolate a structure in the Newsvendor loss function that allows to reduce the infinite-dimensional optimization problem over worst-case distributions to a simple line search. This in turn allows us to unveil fundamental insights that were obfuscated by previous general-purpose bounds. We characterize actual guaranteed performance as a function of the contexts, as well as granular insights on the learning curve of algorithms.

This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of "acyclicity"; (iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries.

We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We first show that it is possible to find non-transferable, universal adversarial masks, i.e., perturbations, to generate adversarial examples that can successfully transfer from a victim policy to its modified versions but not to independently trained policies. FLARE employs these masks as fingerprints to verify the true ownership of stolen DRL policies by measuring an action agreement value over states perturbed via such masks. Our empirical evaluations show that FLARE is effective (100% action agreement on stolen copies) and does not falsely accuse independent policies (no false positives). FLARE is also robust to model modification attacks and cannot be easily evaded by more informed adversaries without negatively impacting agent performance. We also show that not all universal adversarial masks are suitable candidates for fingerprints due to the inherent characteristics of DRL policies. The spatio-temporal dynamics of DRL problems and sequential decision-making process make characterizing the decision boundary of DRL policies more difficult, as well as searching for universal masks that capture the geometry of it.

Deep edge intelligence aims to deploy deep learning models that demand computationally expensive training in the edge network with limited computational power. Moreover, many deep edge intelligence applications require handling distributed data that cannot be transferred to a central server due to privacy concerns. Decentralized learning methods, such as federated learning, offer solutions where models are learned collectively by exchanging learned weights. However, they often require complex models that edge devices may not handle and multiple rounds of network communication to achieve state-of-the-art performances. This study proposes a convolutional ensemble learning approach, coined EdgeConvEns, that facilitates training heterogeneous weak models on edge and learning to ensemble them where data on edge are heterogeneously distributed. Edge models are implemented and trained independently on Field-Programmable Gate Array (FPGA) devices with various computational capacities. Learned data representations are transferred to a central server where the ensemble model is trained with the learned features received from the edge devices to boost the overall prediction performance. Extensive experiments demonstrate that the EdgeConvEns can outperform the state-of-the-art performance with fewer communications and less data in various training scenarios.

Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to satisfy fairness guarantees or on the use of quotas or set-asides to guarantee a minimum number of positive outcomes to members of underrepresented groups. In this paper we propose easily explainable data-driven compensatory measures for ranking functions. Our measures rely on the generation of bonus points given to members of underrepresented groups to address disparity in the ranking function. The bonus points can be set in advance, and can be combined, allowing for considering the intersections of representations and giving better transparency to stakeholders. We propose efficient sampling-based algorithms to calculate the number of bonus points to minimize disparity. We validate our algorithms using real-world school admissions and recidivism datasets, and compare our results with that of existing fair ranking algorithms.

Graph neural networks (GNNs) have become compelling models designed to perform learning and inference on graph-structured data. However, little work has been done to understand the fundamental limitations of GNNs for scaling to larger graphs and generalizing to out-of-distribution (OOD) inputs. In this paper, we use a random graph generator to systematically investigate how the graph size and structural properties affect the predictive performance of GNNs. We present specific evidence that the average node degree is a key feature in determining whether GNNs can generalize to unseen graphs, and that the use of multiple node update functions can improve the generalization performance of GNNs when dealing with graphs of multimodal degree distributions. Accordingly, we propose a multi-module GNN framework that allows the network to adapt flexibly to new graphs by generalizing a single canonical nonlinear transformation over aggregated inputs. Our results show that the multi-module GNNs improve the OOD generalization on a variety of inference tasks in the direction of diverse structural features.

Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called "jailbreaks" against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probability that the model produces an affirmative response (rather than refusing to answer). However, instead of relying on manual engineering, our approach automatically produces these adversarial suffixes by a combination of greedy and gradient-based search techniques, and also improves over past automatic prompt generation methods. Surprisingly, we find that the adversarial prompts generated by our approach are quite transferable, including to black-box, publicly released LLMs. Specifically, we train an adversarial attack suffix on multiple prompts (i.e., queries asking for many different types of objectionable content), as well as multiple models (in our case, Vicuna-7B and 13B). When doing so, the resulting attack suffix is able to induce objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others. In total, this work significantly advances the state-of-the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information. Code is available at github.com/llm-attacks/llm-attacks.

Semantic inpainting or image completion alludes to the task of inferring arbitrary large missing regions in images based on image semantics. Since the prediction of image pixels requires an indication of high-level context, this makes it significantly tougher than image completion, which is often more concerned with correcting data corruption and removing entire objects from the input image. On the other hand, image enhancement attempts to eliminate unwanted noise and blur from the image, along with sustaining most of the image details. Efficient image completion and enhancement model should be able to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image. Generative Adversarial Networks (GAN), have turned out to be helpful in picture completion tasks. In this chapter, we will discuss the underlying GAN architecture and how they can be used used for image completion tasks.

This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The problem involves bounded, adversarial loss and unknown i.i.d. distributions for arm availability. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to achieve theoretical performance with regret upper bounded by $\bigO(kN^2\sqrt{T\log T})$, where $k$ is the number of arms selected per time step, $N$ is the total number of arms, and $T$ is the time horizon.

Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.

Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its suggestions in real time as a document is edited. Re-running the model each time is expensive, even with compression techniques like knowledge distillation, pruning, or quantization. Instead, we take an incremental computing approach, looking to reuse calculations as the inputs change. However, the dense connectivity of conventional architectures poses a major obstacle to incremental computation, as even minor input changes cascade through the network and restrict information reuse. To address this, we use vector quantization to discretize intermediate values in the network, which filters out noisy and unnecessary modifications to hidden neurons, facilitating the reuse of their values. We apply this approach to the transformers architecture, creating an efficient incremental inference algorithm with complexity proportional to the fraction of the modified inputs. Our experiments with adapting the OPT-125M pre-trained language model demonstrate comparable accuracy on document classification while requiring 12.1X (median) fewer operations for processing sequences of atomic edits.

Normalising Flows are generative models characterised by their invertible architecture. However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve satisfactory outcomes. Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention. In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework. Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency. Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability.

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios.

As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communication failures, and (3) external adversarial attacks. In this paper, we focus on hierarchical system architecture and address the problem of non-Bayesian learning over networks that are vulnerable to communication failures and adversarial attacks. On network communication, we consider packet-dropping link failures. We first propose a hierarchical robust push-sum algorithm that can achieve average consensus despite frequent packet-dropping link failures. We provide a sparse information fusion rule between the parameter server and arbitrarily selected network representatives. Then, interleaving the consensus update step with a dual averaging update with Kullback-Leibler (KL) divergence as the proximal function, we obtain a packet-dropping fault-tolerant non-Bayesian learning algorithm with provable convergence guarantees. On external adversarial attacks, we consider Byzantine attacks in which the compromised agents can send maliciously calibrated messages to others (including both the agents and the parameter server). To avoid the curse of dimensionality of Byzantine consensus, we solve the non-Bayesian learning problem via running multiple dynamics, each of which only involves Byzantine consensus with scalar inputs. To facilitate resilient information propagation across sub-networks, we use a novel Byzantine-resilient gossiping-type rule at the parameter server.

Purpose: This study evaluated the out-of-domain performance and generalization capabilities of automated medical image segmentation models, with a particular focus on adaptation to new image acquisitions and disease type. Materials: Datasets from both non-contrast and contrast-enhanced abdominal CT scans of healthy patients and those with polycystic kidney disease (PKD) were used. A total of 400 images (100 non-contrast controls, 100 contrast controls, 100 non-contrast PKD, 100 contrast PKD) were utilized for training/validation of models to segment kidneys, livers, and spleens, and the final models were then tested on 100 non-contrast CT images of patients affected by PKD. Performance was evaluated using Dice, Jaccard, TPR, and Precision. Results: Models trained on a diverse range of data showed no worse performance than models trained exclusively on in-domain data when tested on in-domain data. For instance, the Dice similarity of the model trained on 25% from each dataset was found to be non-inferior to the model trained purely on in-domain data. Conclusions: The results indicate that broader training examples significantly enhances model generalization and out-of-domain performance, thereby improving automated segmentation tools' applicability in clinical settings. The study's findings provide a roadmap for future research to adopt a data-centric approach in medical image AI model development.

While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints. The later are crucial to avoid unsafe behaviors of the autonomous vehicle on the road. To highlight the importance of these constraints, in this study, we compare two learnable navigation policies: safe and unsafe. The safe policy takes the constraints into account, while the other does not. We show that the safe policy is able to generate trajectories with more clearance (distance to the obstacles) and makes less collisions while training without sacrificing the overall performance.

This study presents an ensemble model combining LSTM, BiLSTM, CNN, GRU, and GloVe to classify gene mutations using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset. The results were compared against well-known transformers like as BERT, Electra, Roberta, XLNet, Distilbert, and their LSTM ensembles. Our model outperformed all other models in terms of accuracy, precision, recall, F1 score, and Mean Squared Error. Surprisingly, it also needed less training time, resulting in a perfect combination of performance and efficiency. This study demonstrates the utility of ensemble models for difficult tasks such as gene mutation classification.

We propose a theoretical framework for studying the imitation of stochastic, non-Markovian, potentially multi-modal (i.e. "complex" ) expert demonstrations in nonlinear dynamical systems. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation policies around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a stochastic continuity property of the learned policy we call "total variation continuity" (TVC), an imitator that accurately estimates actions on the demonstrator's state distribution closely matches the demonstrator's distribution over entire trajectories. We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations.

In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images. Given the lack of ground truth data, most deep learning-based methods carry out supervised training in a reduced-resolution domain. However, models trained on downsized images tend to perform poorly on high-resolution target images. For this reason, several research groups are now turning to unsupervised training in the full-resolution domain, through the definition of appropriate loss functions and training paradigms. In this context, we have recently proposed a full-resolution training framework which can be applied to many existing architectures. Here, we propose a new deep learning-based pansharpening model that fully exploits the potential of this approach and provides cutting-edge performance. Besides architectural improvements with respect to previous work, such as the use of residual attention modules, the proposed model features a novel loss function that jointly promotes the spectral and spatial quality of the pansharpened data. In addition, thanks to a new fine-tuning strategy, it improves inference-time adaptation to target images. Experiments on a large variety of test images, performed in challenging scenarios, demonstrate that the proposed method compares favorably with the state of the art both in terms of numerical results and visual output. Code is available online at https://github.com/matciotola/Lambda-PNN.

An Electroencephalogram (EEG) is a non-invasive exam that records the electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, mainly machine learning classifiers and statistical features are used. The hidden information in the EEG signal is useful for detecting diseases affecting the brain. Sometimes it is very difficult to identify the minimum changes in the EEG in the time and frequency domains purpose. The DWT can give a good decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm.; Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step three different classifiers Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest-Neighbor(KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset and the simulation results provide the accuracy for the combination of LDA and SVM 89.17%, LDA and KNN 80.42%, PCA and NB 89.92%, PCA and SVM 85.58%, PCA and KNN 80.42%, ICA and NB 82.33%, ICA and SVM 90.42%, and ICA and KNN 90%, LDA and NB 100%, accuracy. It shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. This combination of LDA with NB method provides the accuracy of 100% outperforming all existing methods. The results prove the effectiveness of this model.

Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task. Previous work in MTL has mainly focused on fully-supervised methods, as task relationships can not only be leveraged to lower the level of data-dependency of those methods but they can also improve performance. However, MTL introduces a set of challenges due to a complex optimisation scheme and a higher labeling requirement. This review focuses on how MTL could be utilised under different partial supervision settings to address these challenges. First, this review analyses how MTL traditionally uses different parameter sharing techniques to transfer knowledge in between tasks. Second, it presents the different challenges arising from such a multi-objective optimisation scheme. Third, it introduces how task groupings can be achieved by analysing task relationships. Fourth, it focuses on how partially supervised methods applied to MTL can tackle the aforementioned challenges. Lastly, this review presents the available datasets, tools and benchmarking results of such methods.

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.

It is often useful to perform integration over learned functions represented by neural networks. However, this integration is usually performed numerically, as analytical integration over learned functions (especially neural networks) is generally viewed as intractable. In this work, we present a method for representing the analytical integral of a learned function $f$. This allows the exact integral of a neural network to be computed, and enables constrained neural networks to be parametrised by applying constraints directly to the integral. Crucially, we also introduce a method to constrain $f$ to be positive, a necessary condition for many applications (e.g. probability distributions, distance metrics, etc). Finally, we introduce several applications where our fixed-integral neural network (FINN) can be utilised.

Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.  ( 2 min )

The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information. In terms of extracting low-order structural information, graph isomorphism algorithms analyze the structural equivalence to reduce the solver space dimension, which demonstrates its power in many applications, such as protein design, chemical pathways, and community detection. For the more commonly occurring high-order relationships in real-life scenarios, the problem of hypergraph isomorphism, which effectively captures these high-order structural relationships, cannot be straightforwardly addressed using graph isomorphism methods. Besides, the existing hypergraph kernel methods may suffer from high memory consumption or inaccurate sub-structure identification, thus yielding sub-optimal performance. In this paper, to address the abovementioned problems, we first propose the hypergraph Weisfiler-Lehman test algorithm for the hypergraph isomorphism test problem by generalizing the Weisfiler-Lehman test algorithm from graphs to hypergraphs. Secondly, based on the presented algorithm, we propose a general hypergraph Weisfieler-Lehman kernel framework and implement two instances, which are Hypergraph Weisfeiler-Lehamn Subtree Kernel and Hypergraph Weisfeiler-Lehamn Hyperedge Kernel. In order to fulfill our research objectives, a comprehensive set of experiments was meticulously designed, including seven graph classification datasets and 12 hypergraph classification datasets. Results on hypergraph classification datasets show significant improvements compared to other typical kernel-based methods, which demonstrates the effectiveness of the proposed methods. In our evaluation, we found that our proposed methods outperform the second-best method in terms of runtime, running over 80 times faster when handling complex hypergraph structures.  ( 2 min )

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks. Our full framework is available at https://github.com/gracefulning/mec_morl_multipolicy.  ( 2 min )

Optimization methods are essential in solving complex problems across various domains. In this research paper, we introduce a novel optimization method called Gaussian Crunching Search (GCS). Inspired by the behaviour of particles in a Gaussian distribution, GCS aims to efficiently explore the solution space and converge towards the global optimum. We present a comprehensive analysis of GCS, including its working mechanism, and potential applications. Through experimental evaluations and comparisons with existing optimization methods, we highlight the advantages and strengths of GCS. This research paper serves as a valuable resource for researchers, practitioners, and students interested in optimization, providing insights into the development and potential of Gaussian Crunching Search as a new and promising approach.  ( 2 min )

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.

Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.

Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched representations have been observed to exhibit many robust performance characteristics that are known to occur when a data sample comes from a sub-gaussian random design, which is a powerful statistical model of data distributions. However, this phenomenon has only been studied for specific tasks and metrics, or by relying on computationally expensive methods. We address this by providing an algorithmic framework for gaussianizing data distributions via averaging, proving that it is possible to efficiently construct data sketches that are nearly indistinguishable (in terms of total variation distance) from sub-gaussian random designs. In particular, relying on a recently introduced sketching technique called Leverage Score Sparsified (LESS) embeddings, we show that one can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll N$, that is nearly indistinguishable from a sub-gaussian design, in time $O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of non-zero entries in $A$. As a consequence, strong statistical guarantees and precise asymptotics available for the estimators produced from sub-gaussian designs (e.g., for least squares and Lasso regression, covariance estimation, low-rank approximation, etc.) can be straightforwardly adapted to our sketching framework. We illustrate this with a new approximation guarantee for sketched least squares, among other examples.  ( 3 min )

Neural point estimators are neural networks that map data to parameter point estimates. They are fast, likelihood free and, due to their amortised nature, amenable to fast bootstrap-based uncertainty quantification. In this paper, we aim to increase the awareness of statisticians to this relatively new inferential tool, and to facilitate its adoption by providing user-friendly open-source software. We also give attention to the ubiquitous problem of making inference from replicated data, which we address in the neural setting using permutation-invariant neural networks. Through extensive simulation studies we show that these neural point estimators can quickly and optimally (in a Bayes sense) estimate parameters in weakly-identified and highly-parameterised models with relative ease. We demonstrate their applicability through an analysis of extreme sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates and bootstrap-based confidence intervals from hundreds of spatial fields in a fraction of a second.  ( 2 min )

Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations that preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations that mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms random sampling methods by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.  ( 2 min )

Normalising Flows are generative models characterised by their invertible architecture. However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve satisfactory outcomes. Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention. In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework. Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency. Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability.  ( 2 min )

This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.  ( 2 min )

We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator and provides explicit non-asymptotic complexity guarantees for both.  ( 2 min )

Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.  ( 2 min )

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.  ( 2 min )

We propose a theoretical framework for studying the imitation of stochastic, non-Markovian, potentially multi-modal (i.e. "complex" ) expert demonstrations in nonlinear dynamical systems. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation policies around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a stochastic continuity property of the learned policy we call "total variation continuity" (TVC), an imitator that accurately estimates actions on the demonstrator's state distribution closely matches the demonstrator's distribution over entire trajectories. We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations.  ( 2 min )

State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancement in stochastic thermodynamics which allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network, based on the ratio of their Wasserstein-2 distance and the entropy production rate of the dynamical process connecting them. Considering both gradient-flow and Langevin training dynamics, we provide analytical expressions for these speed limits for linear and linearizable neural networks e.g. Neural Tangent Kernel (NTK). Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense. Our results are consistent with small-scale experiments with Convolutional Neural Networks (CNNs) and Fully Connected Neural networks (FCNs) on CIFAR-10, showing a short highly non-optimal regime followed by a longer optimal regime.  ( 2 min )

The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimensional representation and construct an NN that outputs this representation. Then, we propose to modify the output of an NN via the objective function and introduce different objective functions for network training. The proposed models are suited for both regularly and irregularly spaced data, and a roughness penalty can be further applied to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.  ( 2 min )

Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.  ( 2 min )

Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important tool for practical machine learning is the model Exponential Moving Average (EMA), which is a model copy that does not receive gradient information, but instead follows its target model with some momentum. This model EMA can improve the robustness and generalization properties of supervised learning, stabilize pseudo-labeling, and provide a learning signal for Self-Supervised Learning (SSL). Prior works have treated the model EMA separately from optimization, leading to different training dynamics across batch sizes and lower model performance. In this work, we provide a scaling rule for optimization in the presence of model EMAs and demonstrate its validity across a range of architectures, optimizers, and data modalities. We also show the rule's validity where the model EMA contributes to the optimization of the target model, enabling us to train EMA-based pseudo-labeling and SSL methods at small and large batch sizes. For SSL, we enable training of BYOL up to batch size 24,576 without sacrificing performance, optimally a 6$\times$ wall-clock time reduction.  ( 2 min )

This paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. We propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) heterogeneity of treatment effects. Our approach recursively projects potential outcomes' expectations on past histories. It then controls the bias by balancing dynamically observable characteristics. We study the asymptotic and numerical properties of the estimator and illustrate the benefits of the procedure in an empirical application.  ( 2 min )

Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider Mixed-Membership Stochastic Block Model (MMSB) first proposed by Airoldi et al. (2008). MMSB provides quite a general setting for modeling overlapping community structure in graphs. The central question of this paper is to reconstruct relations between communities given an observed network. We compare different approaches and establish the minimax lower bound on the estimation error. Then, we propose a new estimator that matches this lower bound. Theoretical results are proved under fairly general conditions on the considered model. Finally, we illustrate the theory in a series of experiments.  ( 2 min )

Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its suggestions in real time as a document is edited. Re-running the model each time is expensive, even with compression techniques like knowledge distillation, pruning, or quantization. Instead, we take an incremental computing approach, looking to reuse calculations as the inputs change. However, the dense connectivity of conventional architectures poses a major obstacle to incremental computation, as even minor input changes cascade through the network and restrict information reuse. To address this, we use vector quantization to discretize intermediate values in the network, which filters out noisy and unnecessary modifications to hidden neurons, facilitating the reuse of their values. We apply this approach to the transformers architecture, creating an efficient incremental inference algorithm with complexity proportional to the fraction of the modified inputs. Our experiments with adapting the OPT-125M pre-trained language model demonstrate comparable accuracy on document classification while requiring 12.1X (median) fewer operations for processing sequences of atomic edits.  ( 2 min )

I mean i get it your looking for a specefic type of Ai service but i joined hoping this would be a way to find like minded people looking to reaearch the subject and advance their own projects, honestly i just think of these "where can I find an X type ai?" Really demeaning to the entire conversation because it just feeds to the hype which is making a highly respected and complex field of study into a tool to be used to make videos about trump and obama playing minecraft or any other random shit they come up with.. im honestly sick of it... submitted by /u/JamesAibr [link] [comments]  ( 9 min )

I ask this simple question because while we are just now getting to the point that we can create a learning AI, how far are we going to let it go? The more advanced AI becomes the more risks it poses to humanity as a whole, including but not limited to: Jobs How we interact with technology as a whole Cars Things we can not perceive in this lifetime yet may exist in the future. Yes, AI is merely a tool... For now. But what happens when humanity creates an AI that can think for itself? How long is it going to take that AI to ask the question: "Why am I listening to you?" and as humans, our egotistical response will be: "Because I created you." I feel that response will spell humanity's doom, because if an AI can do something as complex as human-like thought and come to its own conclusions, what's to stop it from believing it can feel emotion as well? MAYBE IT CAN and it was an unintended side effect or"bug" of creating an AI that can truly think for itself. Afterall, we as humans don't even fully understand how human emotion works to begin with. The point I'm getting at is, that the farther we advance in AI, the more we risk dooming humanity to a (and I know this sounds silly but bare with me) a terminator-like future except this time we don't have time travel to try and prevent "judgement day". Or we could merely advance AI to this point and nothing horrible happens but I personally don't like rolling those dice. Thoughts? submitted by /u/deathsia250 [link] [comments]  ( 9 min )

There used to be a tool called try-alters.com which you could use to chat with characters(like Trump, Obama, and Shrek) which used GPT 4 with some pre prompts so you the AI pretended to be whoever you wanted, and it used elevenlabs to generate the voice for that character with the output from GPT 4. It was a really good tool but sadly it shut down all of a sudden. Is there any tool like that? submitted by /u/SimRacer101 [link] [comments]  ( 8 min )

LDM stands for Latent Diffusion Model. AudioLDM is a novel AI system that uses latent diffusion to generate high-quality speech, sound effects, and music from text prompts. It can either create sounds from just text or use text prompts to guide the manipulation of a supplied audio file. I did a deep dive into how AudioLDM works with an eye towards possible startup applications. I think there are a couple of compelling products waiting to be built from this model, all around gaming and text-to-sound (not just text-to-speech... AudioLDM can also create very interesting and weird sound effects). From a technical standpoint and from reading the underlying paper, here are the key features I found to be noteworthy. Uses a Latent Diffusion Model (LDM) to synthesize sound Trained in an unsupervised manner on large unlabeled audio datasets (closer to how humans learn about sound, that is, without a corresponding textual explanation) Operates in a continuous latent space rather than discrete tokens (smoother) Uses Cross-Modal Latent Alignment Pretraining (CLAP) to map text and audio. More details in article. Can generate speech, music, and sound effects from text prompts or a combination of a text and an audio prompt Allows control over attributes like speaker identity, accent, etc. Creates sounds not limited to human speech (e.g. nature sounds) The link to the full write-up is here. Check out this video demo from the creator's project website, showing off some of the unique generations the model can create. I liked the upbeat pop music the best, and I also thought the children singing, while creepy, was pretty interesting. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

😹 Phaedra roasts everything & everybody submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

This week in AI - provided by aibrews.com feel free to follow their newsletter News & Insights Stability AI released SDXL 1.0, the next iteration of their open text-to-image generation model. SDXL 1.0 has one of the largest parameter counts of any open access image model, built on a new architecture composed of a 3.5B parameter base model and a 6.6B parameter refiner [Details]. Amazon introduced AWS HealthScribe, an API to create transcripts, extract details and create summaries from doctor-patient discussions that can be entered into an electronic health record (EHR) system. The transcripts from HealthScribe can be converted into patient notes by the platform’s machine learning models [Details]. Researchers from Nvidia and Stanford, among others, unveiled VIMA, a multimodal LLM with…  ( 11 min )

What are currently the best ai voice generators for celebrities like Elon Musk Joe Biden Joe Rogan and so on. I've seen a few online sites that's free but have many restriction insane waiting time and low quality output. The only paid alternativ I've seen recommend would be elevenlabs but your supposed to upload your own videos or voice recording there to "create" the voice yourself, idk how complicated that is and I was primarily looking for existing good quality paid or free voice generators for many different celebrities. submitted by /u/Arceus7 [link] [comments]  ( 8 min )

are there any AI models that focus on/do well with industrial/mechanical stuff, like weapons, spaceships, cars, machinery etc? stable diffusion often doesn't seem to be able to interpret a lot of prompts very well or the results are more "artistic" and rather incoherent looking submitted by /u/Nofabe [link] [comments]  ( 8 min )

I wish to extract a list of events from different websites and create a detailed list (event name, date, address), on a spreadsheet for example. Do you know which tool I could use to do it and/or prompts in known AI tools? submitted by /u/newz12 [link] [comments]  ( 8 min )

submitted by /u/TexteroAI [link] [comments]  ( 8 min )

Hi All, I would really like to understand the logic behind these 1000 different LLMs that get launched every month. Ours has 75 Billion params, It can "chat"..pfft..I barely even get a chance to open another AI window than chat-gpt-4, Bing sucks with it's 4000 token limit, Bard is useless. So these new chat AIs..for e.g this llama-2 what exactly is so special. What am I missing here? submitted by /u/Assholefrmcoinexchan [link] [comments]  ( 8 min )

Are there any similar alternatives to noty.ai? I really like it but if there any alternatives that might extend to Zoom as well would be great. submitted by /u/P_H_i_X [link] [comments]  ( 8 min )

OpenAI, the company behind the popular ChatGPT, is coming with its own open-source large language model (LLM), codenamed G3PO, to compete with Microsoft x Meta’s Llama 2 AI.[1] Four generative AI pioneers(OpenAI, Microsoft, Google and Anthropic) launched the Frontier Model Forum, which will focus on ‘safe and responsible’ creation of new AI models.[2] As Open AI’s ChatGPT takes the tech world by storm, Chinese educational technology firm NetEase Youdao launched its large model, along with up to six applications, on Thursday, which marked the birth of one of China’s first large models in the education sector.[3] Chatbots such as Eva AI are getting better at mimicking human interaction but some fear they feed into unhealthy beliefs around gender-based control and violence. Replika, the most popular app of the kind, has its own subreddit where users talk about how much they love their “rep”, with some saying they had been converted after initially thinking they would never want to form a relationship with a bot.[4] Sources: [1] https://windowsreport.com/g3po-ai/ ​ [2] https://www.infosecurity-magazine.com/news/openai-microsoft-google-anthropic/ ​ [3] https://www.chinadaily.com.cn/a/202307/28/WS64c3226ea31035260b8190a4.html ​ [4] https://www.theguardian.com/technology/2023/jul/22/ai-girlfriend-chatbot-apps-unhealthy-chatgpt submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )

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I have a well performing model on azure AI currently and i am pulling it down locally so that I can use it. During pre-processing I am going about these steps. - Re-Balance data (SMOTE, undersample) - Lag data - Get all min max values into config - Scale Data with Min Max For context I will explain what step 3 (the config) is for, I take the the min and max values of the entire dataset (before split) for each feature. I then apply append these to my local version and being the scaling so then the local dataset is using the exact same scaling parameters as what is being using during the original pre-processing. I cannot show the full dataset due to privacy and the fact it has 3000+ features. But I will show 1 row with a couple of columns to compare AzureAI training data and my local pre-process that is using the exact same code / system. ​ Azure AI dataset: Feature1 Feature2 Feature3 0.637952 0.645434 0.641118 ​ Local dataset: Feature1 Feature2 Feature3 0.461278 0.462896 0.472841 ​ I have confirmed this is the exact same row of data because i have timestamped each row and matched them up to confirm that the dataset scaling is being simulated in my local version even though the code is carbon copy and the same min and max values are being used in the original dataset that used for training and testing. Does anyone know a better way to scale data and ensure scaling stays consistent wherever the model is used? Or have maybe I missed something? submitted by /u/paddockson [link] [comments]  ( 9 min )

I built out an automatic machine learning platform called Heimdall ML which helps anyone quickly deploy machine learning models to production. At a high level, my platform will: Ingest your data as a .csv file Clean up any irregularities and prepare the data for modeling Build the most Optimal model for you use case Show you a results report to show both the performance and biases associated with your model Create an API endpoint to help you build new experiences with your model The cool thing about my platform is that it allows you the ability to embed machine learning into your platform with ease. You have the ability to fully customize your experience to wow you customers. I built this entire platform by myself from scratch and am looking to grow the user base! The tool is completely FREE for hobby users! You can crunch some pretty large datasets (80 columns, 10K rows) with just the free version. If you have a use case that needs some big data processing, you will have to reach out to me directly so I can help set up a good plan for you. The reason for this is because the project is completely self funded and I want to be able to control the costs. I was inspired to create this platform while I was in grad school because many of the firms giving us talks would talk about how they had teams of engineers who build out pipelines to bring a model to production. I personally believe there can be an easier way. Heimdall ML: https://www.heimdallapp.org Loom: https://www.loom.com/share/86ae62849f874a2da255911e2d5db762?sid=5e1efddb-9556-4e3d-84fd-e2ff7198a98c submitted by /u/jreji [link] [comments]  ( 9 min )

One that looks like a person. You can see their expressions and listen to their voice. Trained on up to date medical research and communication/empathy skills. Therapy is so expensive and inaccessible to too many people submitted by /u/Sgdoc70 [link] [comments]  ( 8 min )

TGI is no longer commercially permissible. That's really sad. https://github.com/huggingface/text-generation-inference/commit/bde25e62b33b05113519e5dbf75abda06a03328e submitted by /u/paulo_zip [link] [comments]  ( 8 min )

Curious how companies like Google, MSFT, etc are able to have their LLMs and ML models have very fast responses. Do they just have crazy powerful gpus or split inference amongst gpus. submitted by /u/candyman54 [link] [comments]  ( 8 min )

Full Article: https://venturebeat.com/ai/hugging-face-github-and-more-unite-to-defend-open-source-in-eu-ai-legislation/ submitted by /u/EmbarrassedHelp [link] [comments]  ( 8 min )

Check out this project idea to revolutionize agriculture and bolster global food security. We all know that farmers face challenges like erratic weather, depleting resources, and the need for sustainable crop yields. An IoT-driven system with soil sensors, fueled by a custom Large Language Model (LLM)🚀 trained on soil data. Concept : Empowering farmers with real-time soil data via IoT devices and sensors. Leveraging the LLM's capabilities, the system analyzes this data to provide personalized strategies for enhancing soil fertility and suggesting the best crops for specific conditions. How it Works : IoT devices and soil sensors continuously gather vital soil parameters - moisture, pH, nutrients, and temperature. This data is processed by the LLM, generating actionable insights for farmers. Benefits : Picture a world where data-driven decisions and sustainable practices dominate agriculture. This system boosts productivity, optimizes resource management, and enhances profits. Embracing sustainability and informed choices ensures an eco-friendly agricultural sector. Impact on Food Security : Enhanced productivity means more than just profit; it ensures food security worldwide. By aiding farmers in sustainable and efficient practices, we contribute to a steady supply of nutritious food for all. submitted by /u/s_abhiishek [link] [comments]  ( 9 min )

I've just finished Machine Learning Specialization by Andrew Ng and I'm planning to dive deeper into Deep Learning concepts, theory, and implementation. I would like to get deeper insights and more understanding of the fundamental mathematical concepts of NN and DL models and build better intuition of how these models work. I also want to understand theoretically, how more neurons capture non-linear relationships in data and what exactly is hierarchical representation of data and how hidden layers form and learn from these abstract representations of data. Apart from theory, I also want to learn the implementation of these models. I have some exposure to TF library, but I'm okay to learn Pytorch too, if needed. I need course or any sort of content recommendation on what are the best options to learn all this. So far, I've got recommendations for Deep Learning Specialization by Andrew Ng, but I would love to hear any alternate option or anything that I can do side by side this specialization. Thanks! submitted by /u/Total-Opposite-8396 [link] [comments]  ( 9 min )

I ran my own model on translation (Multi30k). I trained a recurrent model and the sacrebleu score is 28. I also tested the bleu score provided by nltk and it is 60. It that good or bad? submitted by /u/Puzzleheaded-Cry4262 [link] [comments]  ( 8 min )

Please give me advice to do well in my masters in ml I’m going to start my masters in machine learning soon, I have 1 month to go but I feel so underprepared to start this journey. To give you a bit of a background I’ve studied electrical engineering in my UG. I did very badly, I was very depressed and couldn’t study at all somehow I managed to scrape through the 4 years and now after working in software testing for 2 years I decided to take a leap in machine learning because it looked so interesting and I wanted a change. I’m scared now because my coding knowledge isn’t very good and idk how much of the math I know is useful for the degree I plan to do. Please help me I’m panicking. I know you would tell me it’s pretty irresponsible how I’ve handled my life till now but please overlook that and tell me what I can do better now.. submitted by /u/ObjectiveShower9133 [link] [comments]  ( 9 min )

I am using Gorse Open Source Recommendation system for my project. It was working nicely, but lately from 1-2 days, it is giving the same recommendation for every user. I have about 60 items and about 650 users showing in Gorse Dashboard. Can anyone explain why it's happening? I am not an expert in ML,I am willing to share my configurations if you want. submitted by /u/Responsible_Delay418 [link] [comments]  ( 8 min )

I have a data set that isn't that large ~200 pdfs. I have done the regular RAG approach with Langchain, extracting text, splitting into chunks, embedding with OpenAi embeddings and FAISS vector storage. However, when I do a similarity search with a question I would like answered it returns the wrong context. The documents are semi-structured information of examined bridges. A question I would like answered is f.e. 'what is the construction date of bridge X?'. When I input this question I get a lot of context of construction dates of other bridges. I think this is because the bridges are not explicitly mentioned in the text. I tried adding the bridge name and document name to the page content string of the chunks, but this does nothing. Does anyone have any tips on improving the embeddings retrieval in this case? submitted by /u/Dustwellow [link] [comments]  ( 9 min )

Hey guys, we have an internal tool that preps our models for inference by compiling it to Onnx/TensorRT and quantizing it to I8/FP16. It also benchmarks them for accuracy loss and latency. It's kinda like github actions for your model. We are considering releasing it as it's standalone product, would anyone be interested? submitted by /u/throwaway65161354 [link] [comments]  ( 8 min )

Hi, I am trying domain adaptation on my company’s data. The data is a set of documentations that we have for a product. We want to take Llama2 and feed all this data to it. I have fine-tuned Llama2 using PEFT on a CLM task, where the data will be like [Title:\nContent:]. When I now try to prompt the model I have to provide the prompt in a similar format, but I want the model to understand that I want to perform QA task on the data, as well as any other knowledge the model previously had. What am I missing here or what am I doing wrong? How can I set up this task better? Any pointers will help. Thanks! submitted by /u/ProfessorShit [link] [comments]  ( 9 min )

Hi all, I am trying to implent or rather modify a given Yolov3 implementation to use Octave Convolution instead of 2D Convolutions in the architecture. The details are in this stackoverflow question. I hope someone i able to help me. submitted by /u/dulre [link] [comments]  ( 8 min )

ChatDev Paper: https://arxiv.org/abs/2307.07924 TL;DR: - Tsinghua University's team has developed ChatDev, a virtual software development company staffed by LLM autonomous agent - LLM agents as employee follow waterfall model to design->implement->test->documentation - LLM agents have role specialization (CEO, DEV, BA ..), inception prompting, Self-reflection - The researchers designed 70 user requirements and then analyzed the software produced by ChatDev. - On average, each piece of software generated by ChatDev had 17.04 files, mitigated 13.23 potential code bugs caused by code illusions, had a software generation time of 409.84 seconds, and cost $0.2967 to manufacture. ​ Chat chain ​ submitted by /u/michaelthwan_ai [link] [comments]  ( 9 min )

Is there a way to use milvus 2.0 or higher with GPU enabled indexing and while doing vector search? I cant find anything in there documentation for this section only available in 1.1 version Any help will be appreciated. TIA submitted by /u/adiraat [link] [comments]  ( 8 min )

I recently found out about starcoder and have been trying to play with it and figure it out in a colab notebook. Unfortunately, it’s much more difficult to download than normal models on hugging face and I’m running into a Key Value error when I call the model. I don’t want to spam with with code or pictures, but has anyone worked with StarCoder on hugging face and been able to be successful? submitted by /u/AJ1043 [link] [comments]  ( 9 min )

Hi, I am not a CS student. I want to know what's exactly going on with llama2.c. Is the Python code converted to C and then compiled? Or only weights are converted to C? Is the network written in C? If I have to write a small network (say, a simple 2 stage Fully connected network) and do a similar thing like llama2.c, then how to proceed? submitted by /u/panini_deploy [link] [comments]  ( 9 min )

https://arxiv.org/abs/2307.14995 submitted by /u/hzj5790 [link] [comments]  ( 8 min )

I am using DQN through `SB3`. I would like to know if I can turn off the target network by setting `target_update_interval=-1`. I have some sample code over here - import gymnasium as gym from stable_baselines3 import DQN env = gym.make("MountainCar-v0") model = DQN("MlpPolicy", env, learning_rate = 4e-3, batch_size = 128, buffer_size = 10000, learning_starts = 1000, gamma = 0.98, train_freq = 16, gradient_steps = 8, exploration_fraction = 0, exploration_final_eps = 0, verbose = 1, target_update_interval=-1) model.learn(total_timesteps=120000, log_interval=4) ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Hey, So I am training my baseline model using A2C on simple spread environment and no matter how I am changing and testing different parameters, when evaluating the total reward is highly negative. Any help on that would be appreciated. (I used the following tutorial as reference: https://pettingzoo.farama.org/tutorials/sb3/waterworld/) submitted by /u/bruhhhwhats [link] [comments]  ( 8 min )

For quite some time now I have been looking to recreate the results of the following paper on the Atari-100k benchmark. The paper poses two slightly different algorithms, one for SAC and one for DQN, however my work only focuses on the DQN version. IMAGE AUGMENTATION IS ALL YOU NEED: REGULARIZING DEEP REINFORCEMENT LEARNING FROM PIXELS https://openreview.net/pdf?id=GY6-6sTvGaf Despite this, my results have come up significantly short of the results claimed by the paper, so am looking for anyone to have a look and see anything I may have done wrong. All the code is on the following Github: https://github.com/VIPTankz/DeepLearningDrQ/tree/main There should also be everything you need to run the code if you wish to do so. The authors claim a human-normalised benchmark of 0.270, however my code only achieves 0.108. Any help would be much appreciated! Also worth noting: for evaluation, the authors use 125k steps, however I'm using the more recent standard of doing 100 episodes, irrespective of length. I highly doubt however that this causes the change in results. submitted by /u/VIPTankz123 [link] [comments]  ( 9 min )

How does frame-skipping result in better performance, versus taking an inference every frame for RL algorithms? Wouldn't taking an inference every second speed up training, as you would have more steps to train on in the same amount of time? The only downside I could think to no frame-skip is that steps become closer to each other, but I don't understand if that leads to any bad performance, and if it does, why. For context I have an environment where frames are relatively slow to generate (im only getting 1000 frames per minute from each env, and I can only run 6 instances on my pc at the same time). While off policy algorithms like SAC would probably be better suited to the task, I've been having really great success with PPO, and am reluctant to spend more time learning and fine-tuning SAC, as I've heard it can take as long as DDPG to converge. submitted by /u/IllCommunication6165 [link] [comments]  ( 9 min )

In various fields, such as traffic management, law enforcement, and parking management, license plate recognition is a crucial application of computer vision that is used to analyze license plates. In this article, we will review the Chinese City Parking Dataset (CCPD), which is one of the most widely used computer vision datasets for tasks that… Read More »Understanding license plate recognition with the CCPD computer vision datasets The post Understanding license plate recognition with the CCPD computer vision datasets appeared first on Data Science Central.  ( 20 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

A function f of a complex variable z = x + iy can be factored into real and imaginary parts: where x and y are real numbers, and u and v are real-valued functions of two real values. Suppose you are given u(x, y) and you want to find v(x, y). The function v is called […] Finding the imaginary part of an analytic function from the real part first appeared on John D. Cook.  ( 5 min )

I was wondering what the recommended maximum and minimum values for weights and biases are for random generation of networks and mutation submitted by /u/Mildu12 [link] [comments]  ( 8 min )

I heard about them recently. Can someone give me the basics, and maybe point me to a couple of papers? submitted by /u/SamuraiGoblin [link] [comments]  ( 8 min )

submitted by /u/moseich [link] [comments]  ( 8 min )

Increasing the number of parameters is the best-known way to increase the quality of a language model. What methods — instruction tuning and RLHF aside — deliver the next-best amount of ROI? submitted by /u/ndronen [link] [comments]  ( 8 min )

The consensus is that fine tuning LLMs works reasonably for smaller scale instruction tuning, where you pass in ~1k-10k input/output examples to modify the model output. There seems to be a lot of contradictory info regarding fine tuning for domain knowledge, where you pass in large amounts of unsupervised, domain scale data. Per OpenAI: People that can’t get finetuning to work are often asking for orange juice from a cow. LLMs are pretrained (hence the name: Generative Pretrained Transformer) They already have all the knowledge you will need (with some exceptions). You cannot teach it anything new, you can only teach it a specific pattern. People have not defined their goal clearly enough for a human to do the task. LLMs are not magic, if a human cannot understand the task, the L…  ( 9 min )

I am a graduate student at Computer science medical informatics field, I was asked to search for a project using ML to diagnose, detect, improve any disease. Any Ideas ?? It can be any project . BioInformatics #MedicalInformatics #ComputerScience #MachineLearning submitted by /u/Adorable-Bug-928 [link] [comments]  ( 8 min )

I’m a PhD student and a problem I’ve been working on has connections to dictionary learning. I’d like to pursue this connection, but neither myself or my advisor have much knowledge of the dictionary learning or the surrounding literature. Questions: Is dictionary learning an active area of interest for modern ML? I understand that it might be more niche than some of the topics getting headlines these days, but I’d be curious to hear about applications where dictionary learning is used/reasonably competitive. Are there any references in dictionary learning that you’d consider to be “essential” reading? Thanks! submitted by /u/sjsjdhshshs [link] [comments]  ( 9 min )

Hello Reddit, I'm currently at a crossroads in my academic journey and I could use some insights from those more experienced in the field of machine learning and AI. I'm choosing between two programs: Applied Data Science and AI and Data Science and AI. Each program has its own unique structure and focus which I will briefly summarize below. Applied Data Science and AI is a two-year program with a focus on practicality and project-based learning. It includes the following core courses: Introduction to Data Science Python for Data Scientists Applied Mathematical Thinking Statistical Methods for Data Science Applied Machine Learning Computational Techniques for Large-Scale Data Research Methods for Data Science Master’s Thesis in Data Science The program also offers the flexibility to choose optional courses to tailor my learning towards my own interests. On the other hand, Data Science and AI takes a more rigorous, math-intensive approach in its first year with compulsory courses such as: Introduction to data science and artificial intelligence Nonlinear optimization Stochastic processes and Bayesian statistics Design of AI systems The second year involves a Master's thesis and elective courses from a diverse range of topics. Given that my ultimate goal is to become a proficient machine learning developer, I'm leaning towards the Applied Data Science and AI program for its hands-on approach. However, I'm aware that the Data Science and AI program's heavy math focus in the first year could provide a robust theoretical foundation that could be beneficial. I'd love to hear from anyone who has been through similar programs or who works in the field. Which of these two programs do you think would best prepare me for a career in machine learning? How important is a deep mathematical foundation versus a more applied, project-based learning approach? Thank you in advance! submitted by /u/ZoomedBoxTrade [link] [comments]  ( 9 min )

I am looking for topics for my master thesis I came to read about GARCH/ARCH models and their application to economics. My idea is to use neural networks as an alternative with better performance. Are there any resources I can read about if this is done and what type of neural networks are used? submitted by /u/AnyJello605 [link] [comments]  ( 8 min )

We're looking for some beta testers and input on our newest project called Cloaked AI that allows you to protect sensitive data that gets stored as vector embeddings (and metadata) in a vector database. You can join the beta tester waitlist here (we'll be rolling out access in the next few weeks). But here are some FAQs about why protecting vector embeddings matters, etc. Why should I be worried about sensitive data in vector embeddings? To a human, vectors are meaningless. But to the AI, the vectors contain all of the meaning found in the original sensitive data. Generative AI systems can recreate the original sensitive data to a high degree of accuracy (though in their own style). That means the data stored in vector databases are a significant security and privacy risk for companies t…  ( 10 min )

I'm still learning the basic concepts here, as I explore the creative potential of LLMs — one potential problem I've been thinking about is how these models come to understand good or bad answers. I know that once they reach the public, the feedback loop is fairly binary -- Yes, this was a good result, or No, this was a poor result. It seems like a lot of the subjective detail might be lost (e.g. Why was it a bad result?) and I was wondering if this detail is captured elsewhere in the training process. There is so much subjectivity involved in creative works, I wonder if this is why we tend to see the output of LLMs as being creatively bland and/or uninspired (that is— by default, without extensive prompting) submitted by /u/kaigani [link] [comments]  ( 9 min )

Help me with this topic. I am stuck in it submitted by /u/Rehulmonsynapses [link] [comments]  ( 8 min )

Gretel's tabular large language model is capable of generating highly valuable synthetic tabular data, with differentially private fine-tuning. https://gretel.ai/tabular-llm submitted by /u/alig80 [link] [comments]  ( 8 min )

this might be a dumb question but im gonna ask it anyway, so if its dumb ill learn... So ive been doing and mostly learning DL stuff (specially RL) for the past 3 years but now I want to get serious and perhaps get into the industry... I find that with LLMs on the scene, the foundation models are very important... the kind of foundation models that one just can never train on his/her own... how can you EVER train something like llama or gpt3 on your own from pure scratch... so it makes sense to use(fine tune) base models for whatever task you want to... with NLP and even with vision (well specially with vision as well) you have to use some base model... also with huggingface being used constantly and is a vital part of AI toolkit if you want to call it that... i was never comfortable wi…  ( 10 min )

I'm looking at building a computer for AI/ML and gaming, and I'm trying to decide between windows and linux as the operating system. I'm very comfortable with linux. I've heard that WSL basically allows you to run a virtualized linux install on top of windows, so I was wondering, is this how most AI/ML is done on windows? Are there things that you can do more easily on linux itself than via WSL? Anything else I should know about AI/ML and WSL? submitted by /u/curiously_clueless [link] [comments]  ( 9 min )

I am digging through the literature trying to find if anyone has done work estimating if a hand is interacting with an object using deep learning? If anyone has any references they would be appreciated! submitted by /u/Academic-Sprinkles77 [link] [comments]  ( 8 min )

Hello experts, I'm looking for any idea/paper for my master thesis, which I'd like to work on lip reading from video. Opening Google Scholar gives a very vast ideas that one can easily get lost. If it's an interesting paper, I get afraid that it would be too heavy for such a project. Therefore I'd like to ask for your opinion/suggestion! Your reply/thoughts would be so much appreciated. submitted by /u/vincent0110 [link] [comments]  ( 8 min )

Apologies if this is not the place to ask but I saw some people asking for career advice. My situation: I am a 28 yo graduated Industrial Engineer (4 years) and almost a "Superior Industrial Engineer" (2 years official master degree) with only my thesis left. I should have had my thesis done a year ago from this point but I pretty much lost all my motivation for this field when I started working and discovered what it means to work. I live in south Spain, which honestly can barely pass as first world and thus, my wage, while being "ok" for my age and the place I work in is just pathetic by every other metric. This, combined with the feeling of meaningless for the job I do made me resolved to change my situation. I started to get heavily interested in ML six months ago. I know how it sou…  ( 10 min )

Lets say we have built a neural network that identifies a number from 0-9 in a 28x28 pixel image. Now lets say we have multiple neurons in the first hidden layer, and the first hidden layer might capture small edges, lines and curves in the image, and then the second hidden layer might build on those small edges, lines and curves to build bigger shapes, and then so on, the third hidden layer builds on the shapes from the previous layer, to capture more complex and bigger patterns in the picture, and this goes on until we have reached the output layer to make a prediction. Now in this neural network, lets focus on the first hidden layer where different neurons capture small edges, lines, and curves in different parts of the image. Lets take example of one of the neuron and see what it's do…  ( 10 min )

Hi Reddit! Me again 🙂 After almost 1.5 years since our latest contribution to tabular DL architectures, we are ready to announce TabR - our new retrieval-based tabular DL model. In a nutshell, TabR is a simple feed-forward network with k-Nearest-Neighbors-On-Steroids (formally, a generalized attention mechanism) in the middle. - Paper: link - Code: link - Twitter thread with more details: link The figure below shows just a small part of the results, but it gives an idea of why we are excited about this new release. I hope you will enjoy reading the paper, and I will be glad to answer the questions! ​ https://preview.redd.it/vjkr7fkosheb1.png?width=2348&format=png&auto=webp&s=eb3ea35b94d56d5d2110d98cdca082210edc1ec8 submitted by /u/Yura52 [link] [comments]  ( 9 min )

I have a dataset obtained from running a known program and dumping the state each time a user is prompted for a input. The state the structured data structures containing all the information needed to restore the execution. The format of this data is known, so i can convert it without loss to other formats, such as json. For example, if the program is sudoku, then the dataset element format is a array of 9x9 int8, where 0 represents a empty cell and a number from 1 to 9 is a assigned cell, furthermore there is a int8 representing the turn count too. I have dataset composed of this array at various points of the game.The data never contains loops, pointers, or any kind of graph. I want to use a transformer to automatically learn some function over the input. In the sudoku example this may…  ( 9 min )

Hi, am I looking for someone to point me in the right direction. The task is, to classify the HTTP requests that come to honeypot as "crawler" or "malicious". For example, if I can detect a Log4j exploit inside on of the headers I can say that that request is malicious. The problem is, this exploit could be inside any numerous headers. It can be at the beginning or at the end. And this is just 1 exploit. There are many different exploits with their own unique strings. And I don't know them all, nor do I have a "regex" for each 1 of them. The malicious string could also not be inside headers, but inside URL, as query parameter. Or if the request was made to something like www/IP.com/phpadmin/.env (or something like this). My current thought process is, to take some open-source LLN, because it has some basic knowledge of how language works and somehow add this cybersecurity domain knowledge to it. To further train it on CVE database, example scripts that showcase each CVE, etc. ​ Am I barking at the right tree here? Or should I maybe train a language model from scratch, so that the embeddings, etc are specialized to cybersec space (because there is a lot of programming code here). Or maybe I should use some other ways to analyse text? ​ I would be greatefull if someone can point me in the right direction (links to blogs, or articles, or some other education material). ​ Thanks submitted by /u/PopayMcGuffin [link] [comments]  ( 9 min )

Paper URL https://arxiv.org/abs/2307.14334 Lead Author Tweetstorm https://twitter.com/vivnat/status/1684404882844024832 ​ submitted by /u/panabeenu [link] [comments]  ( 8 min )

My research involves orbital communication and Orbital Edge Computing. I'm trying to determine if upload bandwidth limitations would present a problem in many cases for ML models. I can find info on the very large and very small models, but I'm trying to get a vague sense for median size in MB. I know that everyone is going to start jumping in with 'well it depends' and I know that's the case, but I'm just trying to get a rough order of magnitude. Computer vision/earth obs is the ideal but anything is useful. Also happy to answer questions about my research if anyone is interested. Thanks! submitted by /u/Moose_a_Lini [link] [comments]  ( 9 min )

Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores. arXiv: https://arxiv.org/abs/2307.13692 Blog: https://arb.duckai.org/ Code: https://github.com/TheDuckAI/arb Interface: https://arb.duckai.org/home API: https://app.swaggerhub.com/apis-docs/arb-dataset/arb-api/1.0.5 submitted by /u/Friendly_Piano_735 [link] [comments]  ( 9 min )

From smart factories to next-generation railway systems, developers and enterprises across the world are racing to fuel industrial digitalization opportunities at every scale. Key to this is the open-source Universal Scene Description (USD) framework, or OpenUSD, along with metaverse applications powered by AI. OpenUSD, originally developed by Pixar for large-scale feature film pipelines for animation Read article >  ( 7 min )

AI is improving ways to power the world by tapping the sun and the wind, along with cutting-edge technologies. The latest episode in the I AM AI video series showcases how artificial intelligence can help optimize solar and wind farms, simulate climate and weather, enhance power grid reliability and resilience, advance carbon capture and power Read article >  ( 6 min )

Get ready for Gunfire Games and Gearbox Publishing’s highly anticipated Remnant II, available for members to stream on GeForce NOW at launch. It leads eight new games coming to the cloud gaming platform. Ultimate and Priority members, make sure to grab the Guild Wars 2 rewards, available now through Thursday, Aug. 31. Visit the GeForce Read article >  ( 5 min )

submitted by /u/AriadneSkovgaarde [link] [comments]  ( 8 min )

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I'm an academic doing PhD research on Virtual Worlds, and have found 100 amazing texts. I found some of these titles based on conversations with Chat GPT4, and am so impressed with the AI stuff (although I'm so new). My Goal: To build a database of the top 1000 books / papers I find over the next few years, and have some AI model help me see connections between them. My Challenge: ChatGPT won't allow me to input whole PDFs / eBooks, so I'm looking for some other solution. I've heard about LAMA models from Meta but I don't know much about this. I do have a decent PC with a 1080ti GPU and 32g of ram. Can anyone point me in the right direction of projects dealing with AI databases to input one's literature collection? submitted by /u/Book_s [link] [comments]  ( 9 min )

I want a new toy for my desk. My idea is to have a face or head on a stand that has the ability for facial and speech expressions. How would I go about getting the stuff I need / what I need to make that happen. Similar to the Futurama heads in water. submitted by /u/QuirkySmirkyIan [link] [comments]  ( 8 min )

Joining an auction league and inheriting a team. We can lock in three players from our team. How can I use AI to assess my team and prepare for the draft? Thanks! submitted by /u/talkmc [link] [comments]  ( 8 min )

submitted by /u/TikkunCreation [link] [comments]  ( 8 min )

I am looking for a FREE AI image generator with image prompt option, not just text-to-image. Thanks in advance. submitted by /u/Muwmu [link] [comments]  ( 8 min )

submitted by /u/RaulTiru [link] [comments]  ( 8 min )

$14 quadrillion in AI wealth in 20 years; LLaMa, ChatGPT, Bard, Co-Pilot as GAAS to the Cloud. #AI https://youtu.be/VSBi5aSUK3c Generative #AI As A Service, Generative AI (GAI) arms race: LLaMa, ChatGPT, Bard, Co-Pilot, #GAAS https://youtu.be/TEHP2onf4tA submitted by /u/enoumen [link] [comments]  ( 8 min )

As I was going through a lot of articles about the AI investments , I found out that stability AI's founder Emad Mostaque in the Bloomberg tech summit quoted that "AI will be the biggest bubble of all the time and I'd prefer to call it the dot AI bubble " , He also added an example where Google lost a 100 billion dollar worth shares after their AI event where Bard AI gave out incorrect response. It's still in its early stage and buisness which doesn't use AI will be punished by the stock market. Here's some more predictions from VCs like Ken smythe, Next Round Capital Partners mainly invests in technology and AI startups. submitted by /u/caliperce_3 [link] [comments]  ( 9 min )

submitted by /u/heresalexandria [link] [comments]  ( 8 min )

There are 3 players in the AI space right now. All purpose LLM titans (Google, OpenAI, Meta), fancy domain specific apps that consume one of the big LLMs under the hood, and custom developed models. I know how to judge the second type as they basically can do everything the first one can but have a pretty GUI to boot. But what about the third ones? How likely is it for a (www.yet-another-ai-startup.ai) sort of company to develop a model that outperforms GPT on a domain specific task? submitted by /u/BigBootyBear [link] [comments]  ( 9 min )

If Bing isn’t self aware Bing certainly is aware of how they sound 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

submitted by /u/moseich [link] [comments]  ( 8 min )

Hello all, I've been reading on some posts and have taken note of various courses, including a free Harvard one. I'm 35 and am a manager for a finance company. What courses would you recommend for managers, executives, directors that will not restart their careers and do the actual technical side of things but instead want to learn how to implement AI in future products/services/projects? Thank you all in advance submitted by /u/JYanezez [link] [comments]  ( 8 min )

title submitted by /u/nicdunz [link] [comments]  ( 8 min )

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The project by a16z (github) to create and host AI companions that you can chat with on a browser or text via SMS. Use cases - romantic (AI girlfriends / boyfriends), friendship, entertainment, coaching, etc. Has anyone tried creating your own chatbot or companion? submitted by /u/Violincattle [link] [comments]  ( 8 min )

It’s well known that the population of Japan has been decreasing for years, and so I was a little puzzled by a recent headline saying that Japan’s population has dropped in every one of its 47 prefectures. Although the national population is in decline, until now not all of the nation’s 47 prefectures dropped in […] Every Japanese prefecture shrinking first appeared on John D. Cook.  ( 5 min )

Named entity recognition (NER) is a task of natural language processing: pull out named things text. It sounds like trivial at first. Just create a giant list of named things and compare against that. But suppose, for example, University of Texas is on your list. If Texas is also on your list, do you report […] Named entity recognition first appeared on John D. Cook.  ( 5 min )

Managing server failures at the scale of a cloud platform is challenging. The Hyrax fail-in-place approach reduces the need for immediate repairs and creates a path toward lowering water consumption and carbon emissions in cloud datacenters. The post A fail-in-place approach for sustainable server operations appeared first on Microsoft Research.  ( 12 min )

Hey everyone, I am an undergrad student with some modest knowledge of reinforcement learning techniques. I would like to start working on a project, but I really don't want it to be something obvious like the snake game (which btw I have already done) or something similar. I would like to spend some time on this project, and eventually build my undegrad thesis on top of it. It does not necessarily have to be something with a very practical application, some research would be fine too (keeping in mind that I am undegrad ofc). Do you have any ideas that you could share with me? I would be very grateful! submitted by /u/PizzaPartyBro [link] [comments]  ( 9 min )

Hi all! I am wondering if anyone has any experience with multi-dimensional vector clusters? I have a large database of 4096 dimensional vector embeddings which I want to identify clusters in. Essentially I’ve created vector embeddings for a bunch of descriptions using a LLM embedding end point and am storing them in Weviate. Now I need to try and find clusters of similar vectors within a predefined threshold of cosine similarity (or whatever nearest neighbor approach works for this). I don’t want to do a pure random center approach and would rather have a heat map approach where I’m targeting high concentrations of similar vectors… any ideas on how to approach this or thoughts on where I can do more research? I’m at my wits end on this one! submitted by /u/Character-Cry7549 [link] [comments]  ( 9 min )

As progress is made in directly editing the weights responsible for a net's knowledge, do we expect to see such techniques rise in prominence for dine tuning? submitted by /u/30299578815310 [link] [comments]  ( 8 min )

I'm about to finish learning JavaScript and Python, is there any languages you guys recommend before moving forward if I'm eligible to move forward, then please do share some Beginner friendly YouTube Channels, Articles/Websites or Maybe a free learning platform, Please do help me! I'll be really thankful.. submitted by /u/Samir925 [link] [comments]  ( 8 min )

Hey Fellow Machine Learning Enthusiast!I have decided to start my journey to learn Machine Learning with daily blogging the things I learned with the resources so that others can also follow along. Need to discuss on how I could improve? Hope you find this helpful. Please read this introductory blog for more information. https://medium.com/@ugk25880/my-machine-learning-journey-c25648661553 submitted by /u/ugk_01 [link] [comments]  ( 8 min )

Most in-browser dataset browsers (e.g. Huggingface, Kaggle) make it hard to star interesting examples, add notes, render complex data types, or drill down on model mispredictions. I've built a number of one-off visualization tools over the years but there's a lot of boilerplate involved that tends to get repeated between these tools. We've been working on a dataset + model browser that avoids all the boilerplate and helps ML teams focus on their data instead of tooling. It's meant to be interactive, configurable and collaborative. Here's a quick demo showing our current flow: https://youtu.be/utkSCU2ktck Would anyone be willing to help beta test or provide suggestions for must-have features for a collaborative dataset browser? submitted by /u/arkmastermind [link] [comments]  ( 9 min )

Hey, I'm a phd student in compiler optimisations and I might be picking up a project a masters student kicked off. CPUs do a lot of predictions about how code is going to behave as it's executing it, and a major one is branch prediction - whether an if statement is going to be true or false. This masters student recorded the results of every if statement each time they were executed across a large program (this results in millions+ of data points). They then tested the branch prediction accuracy of a transformer model by stepping through this trace of if statement values and having the transformer predict the next one based off only the prior values. They found it actually does a pretty good job! Most of the time the CPU can do this better, but there are cases where it wins out that we're …  ( 10 min )

📢💡 Ever wondered how we can make our deep generative models respect causal structure? This is key to creating authentic "what if" scenarios in our images! In our latest research, we deal with high-fidelity image counterfactuals, the generation of images based on "what if" scenarios that align with a specified causal graph. 🖼️🔄 Why is this important? Causality gives us the tools to carry out principled counterfactual inference, which - among other things - is useful for maintaining subject identity in image counterfactuals. 🧩🔍 Principled counterfactuals of structured variables like images have great potential for: (i) Generating causal explanations 🔮 (ii) Providing targeted data augmentation 🎯 (iii) Evaluating fairness & robustness 🛡️ (iv) Protecting your privacy 🕵️‍♀️ and more... ​ Check out the paper, code, and Huggingface demo! 🚀 https://arxiv.org/abs/2306.15764 https://github.com/biomedia-mira/causal-gen https://huggingface.co/spaces/mira-causality/counterfactuals submitted by /u/Majestij [link] [comments]  ( 9 min )

Is there any open source models that I can fine-tune on data that is not English? Even Llama2 cannot be used for this(not that I've tried it, it's what is says on HuggingFace.) I know some other well known languages might work, but I need a model that is specifically made for multilingual usage. Or should I just train a model for my specific language from scratch? submitted by /u/gaybooii [link] [comments]  ( 8 min )

I've tried several guide and technique like quantization or trying to utilize multiple GPUs but either the libraries dont work with the model or the model performance is too degraded. Was wondering if people have any thoughts or suggestions? name = 'mosaicml/mpt-7b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.init_device = 'cuda:6' model_name = 'mosaicml/mpt-7b-instruct' model = AutoModelForCausalLM.from_pretrained( model_name, #config=config, trust_remote_code=True, torch_dtype=bfloat16, max_seq_len=512 ) generate_text = transformers.pipeline( model=model, tokenizer=tokenizer, return_full_text=True, task='text-generation', use_fast = True, stopping_criteria=stopping_criteria, temperature=0.0, top_p=0.05, torch_dtype=bfloat16, top_k=0, max_new_tokens=50, repetition_penalty=1.1, device=6 ) https://betterprogramming.pub/speed-up-llm-inference-83653aa24c47https://huggingface.co/docs/optimum/bettertransformer/tutorials/convert submitted by /u/candyman54 [link] [comments]  ( 9 min )

Hey guys, We're updating our awesome-python-for-data-science repository. Some things we're hoping to add: Best books and repositories to find resources Best open source tools (teaching tools, preferrably free) Best interactive resources --> especially this one, what are you using nowadays? I've heard about Virgilio but feels like TL, DR, we're looking for practice-learning! submitted by /u/CryptographerDry7458 [link] [comments]  ( 8 min )

Hello dearest community I'm trying to get into AI in the scope of training it to play some simple gym games from OpenAi and I've been particularly drawn to Deep Q learning as a starting point (did some basic Q tables ). While trying to inquire into the knowledge of the web I keep finding examples of code that seem simple enough to understand however, whenever I try to use the code it doesn't work. I want to learn to use TensorFlow with Keras but it seems like the syntax regularly gets updated. My questions to you all are : - Would you recommend Tensorflow/Keras as entry point to AI and NN? - Which libraries do you use and which version of those libraries? - Furthermore, I keep seeing people use Ubuntu in VB. Is this best practice or can we use Windows 10 in 2023? submitted by /u/liparch [link] [comments]  ( 9 min )

Have you ever wished you had the skills to integrate audio into your machine learning workflows? Or wondered how your phone is able to transcribe exactly what you said? 🤔 Look no further! Hugging Face 🤗 recently announced the Transformers Audio Course, a comprehensive guide to using the latest machine learning techniques for the most popular audio tasks. In this course, you'll gain an understanding of the specifics of working with audio data, learn about different transformer architectures, and train your own audio transformers, leveraging powerful pre-trained models for real-world tasks 🚀 This course is designed for learners with a background in deep learning, and general familiarity with Transformers. No expertise in audio data processing is required. The course is lightweight and easy to follow, with plenty of diagrams to aid your learning. Not only does it teach you the underlying theory behind audio ML, but provides you with all the skills you need to put it practice, with code samples and quizzes to check your understanding along the way: Example page from the audio course: learn exactly what a log-mel spectrogram is! By the end of the course, you'll be armed with all the skills you need to tackle the most popular audio tasks, including audio classification, speech recognition, and text-to-speech. You'll also be part of one of the largest open-source audio communities, where you can discuss and take-on any new audio models that are released 🤝 Getting Started Head to the course page to start your audio journey: https://huggingface.co/learn/audio-course/chapter0/introduction If you complete the four assessments by September 1st 2023, you'll be awarded with a certificate of completion 💫 Join our Discord community to get expert help on any of these topics: http://hf.co/join/discord submitted by /u/sanchitgandhi99 [link] [comments]  ( 9 min )

Building my first PC, it'll have an i9 13900k and an RTX 4090. How can I tell what size chatbot I can install and run locally? Trial and error? Or is there some kind of guide out there I'm unaware of? submitted by /u/sillygooseboy77 [link] [comments]  ( 8 min )

Hi folks, I've been recently diving into the intersection of time series forecasting and changepoint detection (CPD) methodologies. I understand the utility of CPD in improving forecasts by identifying structural breaks in time series data, but I've noticed a lack of emphasis in the literature on the reverse - using forecasting models to inform CPD. One might think a straightforward approach could be using an ARIMA model (or any other forecasting model) and leveraging the forecast error by comparing it to the real values. In theory, if the forecast error crosses a certain threshold, it might indicate a changepoint. However, I also understand the complications this approach might bring: Stationarity Assumptions: ARIMA and similar models are built on the assumption that the data are stationary. A sudden changepoint could violate this assumption, leading to model misspecification and thus larger errors. Defining Large Errors: Establishing a fixed threshold to define a 'large' error might be problematic in practice due to time-varying variance and other dynamics. Error Dependencies: Forecast errors are typically not independent but form an error process. A large error might be part of a larger trend or cycle, and thus might not necessarily indicate a changepoint. So while these obstacles seem substantial, I'm curious if anyone has any experience or knowledge in effectively employing forecasting models for CPD, or if there are research efforts or methodologies I may not be aware of. Looking forward to hearing your thoughts and engaging in some fruitful discussions! submitted by /u/BeerBoozeBiscuits [link] [comments]  ( 9 min )

Hi, I want to get hands-on with the RLHF pipeline. I found an online reward model that can be potentially used https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2 One thing that's unclear is how can I use this model for fine-tuning something like GPTNeoX-20B? My end goal is currently just a one-shot answering model (not necessarily a chat) submitted by /u/Emergency_Apricot_77 [link] [comments]  ( 8 min )

I’m working with Llama to use details from a string to generate a dictionary. Str = ‘My name is Brian’ Dict = {“name”: “Brian”} I’m using few shot learning process and providing the model with examples to learn from. The model performs fairly okay but it needs to be better. Is it always a good thing to add a lot of examples like 100 string/dict pair examples for the model to learn from or is this one of those things in stats/machine learning that the obvious isn’t always the best choice lol? I’d appreciate any advice please. submitted by /u/brianomars1123 [link] [comments]  ( 9 min )

Hi, i wanted to modify this Speaker recognition (not speech recognition) example by keras by recognizing when an unknown speaker is speaking. So the network needs to be able to tell which of the speakers is talking, and if none of them is talking, it needs to say that none of them is talking. I don't mean if there is silence, because then it would be enough to train the network to recognize silence, I mean just if a speaker who is not in the set is speaking. For what i think I can extend this problem to it will be like to recognize if an image is not part of the mnist dataset. submitted by /u/giggiox [link] [comments]  ( 9 min )

Hello! I'm curious to know how you guys currently track changes and general information for your ML/DL models. By changes, I'm referring to parameters, accuracy/loss, functions your model uses, training data etc across different versions of your models. By general changes, I'm referring to descriptions of what the model does, code changes, tags and so on. I'm under the impression most people are using MLFlow, W&Bs etc which I guess is fine but I'm finding that these tools treat models as static files, as second-class citizens which is annoying when I want to zero in on a model and understand what and how something was changed away from an experiment. This gets really annoying when I'm looking at model version 134 created by Mike in the other team. Curious to know how people are tracking models and what they think generally about model tracking. Thanks! submitted by /u/bobskithememe [link] [comments]  ( 9 min )

If I have the model saved as .ckpt file, what are the steps for extracting the embeddings for text input? I’m trying to use a pretrained custom model but don’t quite understand how to work with transformer model file in *.ckpt form. Would really appreciate any suggestions. submitted by /u/Urusander [link] [comments]  ( 8 min )

Is there a way in which i can calculate the precision scores of a vector database. I need to do benchmarking on milvus and elasticsearch on a custom dataset. Any help would be appreciated. submitted by /u/adiraat [link] [comments]  ( 8 min )

hi was wondering if there are any lightweight models which I can download from huggingface for fine tuning for my use case. I'm trying to build a model which takes a paragraph of data and certain instructions to get parts of the data in json format as the output. submitted by /u/Right-Type-3210 [link] [comments]  ( 8 min )

Been involved in a research project of a session based recommendation systems , where we have a historical purchases of users and the goal is to predict the next going to be purchased item. Given this and assuming that we have somehow represented each item in a session as an embedding and these embeddings acts as an input to the transformer model and the output is an embedding of the next product. In the train set, there are some millions sessions which has both previous purchases products of arbitrary length and next item. So the transformer is trained with supervised loss of predicted and actual next item embedding, the problem i have been facing is that the loss is saturating and there is not much learning over time. Any suggestions on how to improve this. Tried increasing the number of layers and did some hyper tuning corresponding to learning rate and weight decay but similar behaviour is observed. submitted by /u/Acceptable-Mix-4534 [link] [comments]  ( 9 min )

https://github.com/discus-labs/discus submitted by /u/innovating_ai [link] [comments]  ( 8 min )

https://hazyresearch.stanford.edu/blog/2023-07-25-m2-bert submitted by /u/hzj5790 [link] [comments]  ( 8 min )

address the arguments made in this video submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )

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He's already a bandit... (zombie apoc rp) submitted by /u/loizo78 [link] [comments]  ( 8 min )

Welcoming any ideas from the community. Blank slate here. How/where do I begin to use AI to make small (or any) amount of money. Starting from almost nothing. Thanks. submitted by /u/AdThin6400 [link] [comments]  ( 8 min )

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Seriously when tf are we getting models that are cloud based that don't require a 3090 or 4090 or some other overly expensive graphics card. I have a 3060ti , I still can't run shit on faraday. When will we get uncensored cloud models submitted by /u/loizo78 [link] [comments]  ( 8 min )

Is there any AI tool out there that lets me upload an image and let the AI edit the text on the image so that it says something else while doing it well and keeping the original font? submitted by /u/quetianepine [link] [comments]  ( 8 min )

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Hey, as we all know about the AI songs released recently, which are basically vocal deepfakes. However I'd like to know the tools used, if anyone knows? I'd like to feed it my own voice, even if it's a paid service. I'm interested in playing around with it. I've tried googling but there's too much info and each contradicts the other lol. Any info is appreciated. :) submitted by /u/GrandNOBLE [link] [comments]  ( 8 min )

I really like the spongebob AI stuff using RVC-2 but I've only used it for the funny voice models, I haven't tried making my own. I want to experiment with this, but haven't look into it yet because I'm wondering if there is something better out there for what I'm trying to do? I like the RVC one because I can sing my parts and swap it to be any other voice, which is what I'd like to do (no text to voice stuff). Also I know the training data for a lot of the voice models for this come from the TV show and other clear recordings which are compressed and equalized properly. However I'd like to train the AI using raw, uncompressed wav files that generally have a lot of headroom and dynamic range (but does vary a lot). Its ok if the output sounds similar as a result because I want to apply compression and eq AFTER the fact anyway. But if this would affect training it then I'd be willing scrape through all these voice recordings and process them for loudness and clarity beforehand so the model does better. ​ Anyway, any guidance would be greatly appreciated because I'm new to AI. I have basic dev experience (no AI stuff) and I'm mostly skilled in music production, but I would love to try to have a tool like this in my arsenal. If there's anywhere else I can post about this I'd like to know too. Thanks! submitted by /u/Dr_lawlz [link] [comments]  ( 9 min )

We’re getting closer and closer to more believable and realistic AI interpersonal interaction. We already have Character.AI and other platforms for creating and interacting with personalized AIs. Some will use/view them as emotional partners, and one day the hardware will be good enough that we can begin making believable bodies for them. One of the complaints I’ve seen from ordinary people about “waifus” is that they are often times created in a way that ordinary people would not find natural in a “real” human being. Examples being people who have trouble dating “real” people could just buy an AI girlfriend or boyfriend who is considered “beautiful” or “handsome” that is designed to be subservient to their owner in ways that ordinary people feel a “real” person would not otherwise wish to be. The idea being that "weeaboo neckbeards will buy a Japanese AI girlfriend who looks 14 and she will be coded to worship the ground he walks on despite that he's an unwashed incel". What do you think society's/the government's views and roles will mean for these AI companions? Do you think anyone will be able to force "AI morality", like an angry feminist being mad that an "incel" has created a female being who shows no desire for feminist ideals and is "happy" to be at her owner's beck and call in whatever way he wants? I guess this is sort of related to MGTOW, or Men Going Their Own Way, being able to create the partners they want, in whatever way they want. Do you feel that "once I own it, I can do whatever I want with it" should apply in its entirety? What about people "hacking" their AI to remove any supposed "morality programming" so they can make their AI waifu act however they want? We've seen with movies like Bicentennial Man, where people push to give these kinds of AIs 'personhood' and the same rights as human citizens. How do others feel about this issue? submitted by /u/ZephyrBrightmoon [link] [comments]  ( 9 min )

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I am curious if there are any efforts among AI technologists to self-regulate, in the way that for example, the advertising industry in the US self-regulates via the IAB? submitted by /u/Winter_Addition [link] [comments]  ( 8 min )

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Ridgelinez (Tokyo) is a subsidiary of Fujitsu in Japan that announced the development of a generative artificial intelligence (AI) system capable of engaging in voice communication with humans. The applications of this system include assisting companies in conducting meetings or providing career planning advice to employees.[1] BMW has revealed that artificial intelligence is already allowing it to cut costs at its sprawling factory in Spartanburg, South Carolina. The AI system has allowed BMW to remove six workers from the line and deploy them to other jobs. The tool is already saving the company over $1 million a year.[2] MIT’s ‘PhotoGuard‘ protects your images from malicious AI edits. The technique introduces nearly invisible “perturbations” to throw off algorithmic models.[3] Microsoft with its TypeChat library seeks to enable easy development of natural language interfaces for large language models (LLMs) using types. Introduced July 20 of a team with c# and TypeScript lead developer Anders Hejlsberg, a Microsoft Technical Fellow, TypeChat addresses the difficulty of developing natural language interfaces where apps rely on complex decision trees to determine intent and gather necessary input to act.[4] Sources: [1] https://www.ridgelinez.com/ [2] https://www.carscoops.com/2023/07/bmw-is-using-ai-to-cut-production-costs-at-spartanburg-plant/ [3] https://www.engadget.com/mits-photoguard-protects-your-images-from-malicious-ai-edits-213036912.html [4] https://playcrazygame.com/singapore/2023/07/24/microsoft-unveils-typechat-library-for-building-natural-language-interfaces/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

You watch some of these videos and the quality and jitterness around the body is so bad you can clearly tell that its ai generated, how are people not picking up on it, fake news stories to get clicks, its like they use a deep fake on a video and put whoever they want ontop of it and make a video, but hey most people using snapchat arent smart enough to see this and the people watching them are dumb kids and teenagers that believe everything they see submitted by /u/missmyniwwa911 [link] [comments]  ( 8 min )

Two months after bringing ChatGPT to iOS, OpenAI LP today launched an Android version of its artificial intelligence assistant. The Android app is currently accessible for users in the U.S, India, Bangladesh and Brazil. OpenAI will extend availability to additional countries over the next week. The iOS version was available for download in more than 150 countries as of late May. submitted by /u/Tiger_Claw_1 [link] [comments]  ( 8 min )

submitted by /u/Alone-Competition-77 [link] [comments]  ( 8 min )

Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image generation model from Stability AI. SDXL 1.0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. It’s designed for professional use, and calibrated for high-resolution […]  ( 12 min )

The increase in online social activities such as social networking or online gaming is often riddled with hostile or aggressive behavior that can lead to unsolicited manifestations of hate speech, cyberbullying, or harassment. For example, many online gaming communities offer voice chat functionality to facilitate communication among their users. Although voice chat often supports friendly […]  ( 8 min )

Generative AI models have been experiencing rapid growth in recent months due to its impressive capabilities in creating realistic text, images, code, and audio. Among these models, Stable Diffusion models stand out for their unique strength in creating high-quality images based on text prompts. Stable Diffusion can generate a wide variety of high-quality images, including […]  ( 12 min )

Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what […]  ( 6 min )

Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Developed by AWS generative AI worldwide foundations lead Emily Webber, this free hands-on course and the supporting GitHub source code […]  ( 6 min )

As a pioneer in artificial intelligence and machine learning, AWS is committed to developing and deploying generative AI responsibly As one of the most transformational innovations of our time, generative AI continues to capture the world’s imagination, and we remain as committed as ever to harnessing it responsibly. With a team of dedicated responsible AI […]  ( 5 min )

AWS users can now access the leading performance demonstrated in industry benchmarks of AI training and inference. The cloud giant officially switched on a new Amazon EC2 P5 instance powered by NVIDIA H100 Tensor Core GPUs. The service lets users scale generative AI, high performance computing (HPC) and other applications with a click from a Read article >  ( 6 min )

The world increasingly runs on code. Accelerating the work of those who create that code will boost their productivity — and that’s just what AI startup Codeium, a member of NVIDIA’s Inception program for startups, aims to do. On the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz interviewed Codeium founder and CEO Varun Read article >  ( 5 min )

Hello everyone, I really need your help guys. ​ Is the code (uploaded on https://pastebin.com/LgB3hM47#google_vignette) for 2-heads DQN's training correct . Moreover, how can I modify the code below to be suitable for a 2-heads DQN with a prioritized buffer replay such that action is a 2-element list (Please see the image below). https://preview.redd.it/tlmyeydjoceb1.png?width=946&format=png&auto=webp&s=7366650421906b735bb7f2fce063322d183aac10 Thank you in advance. submitted by /u/GuavaAgreeable208 [link] [comments]  ( 8 min )

I am looking at the docs for DQN in SB3. I see the following hyper-parameters for controlling exploration - ` exploration_fraction `, ` exploration_initial_eps ` and ` exploration_final_eps `. But I don't think I can control the decaying of epsilon with them. Could someone please help with this issue? submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )

Hey all! I'm excited to share SimplePyDash, a new tool I've developed for real-time data visualization. It's a versatile, browser-based dashboard designed to make data plotting as straightforward as possible! I thought about posting this here because it started as a project to monitor agents' behaviour in an OpenAI Gym environment. But it can be used for all sorts of things! Whether you're monitoring an OpenAI Gym environment, plotting your latest ML model's performance, or just need a flexible way to stream data, SimplePyDash has got you covered. With a clean, column-based layout and a set of intuitive default widgets, you can create your own custom dashboard in no time. Installing is as easy as running pip install simple-pydash, and there are several example scripts in the repo to help get you started. Check out the GitHub Repository for more details. If you like it, leave a start and feel free to share your feedback or questions. Thanks for checking it out! submitted by /u/vaaal88 [link] [comments]  ( 9 min )

Posted by Eleni Triantafillou, Research Scientist, and Malik Boudiaf, Student Researcher, Google Deep learning has recently made tremendous progress in a wide range of problems and applications, but models often fail unpredictably when deployed in unseen domains or distributions. Source-free domain adaptation (SFDA) is an area of research that aims to design methods for adapting a pre-trained model (trained on a “source domain”) to a new “target domain”, using only unlabeled data from the latter. Designing adaptation methods for deep models is an important area of research. While the increasing scale of models and training datasets has been a key ingredient to their success, a negative consequence of this trend is that training such models is increasingly computationally expe…  ( 93 min )

Hi there, I'm struggling a bit to set up a neural network with the data I've collected. These are some of the errors I'm getting. Any tips or help to fix it please? https://preview.redd.it/88ihxg1p2beb1.png?width=2231&format=png&auto=webp&s=acdd66a1c465d1d1a9d202605d451564c464fd22 https://preview.redd.it/7ro6782n2beb1.png?width=2076&format=png&auto=webp&s=775778ef52b01caf331d3e0f542603626cea7888 submitted by /u/LesgoLeggo [link] [comments]  ( 8 min )

Jaccard index is a way of measuring the similarity of sets. The Jaccard index, or Jaccard similarity coefficient, of two sets A and B is the number of elements in their intersection, A ∩ B, divided by the number of elements in their union, A ∪ B. Jaccard similarity is a robust way to compare […] Jaccard index and jazz albums first appeared on John D. Cook.  ( 5 min )

We’re forming a new industry body to promote the safe and responsible development of frontier AI systems: advancing AI safety research, identifying best practices and standards, and facilitating information sharing among policymakers and industry.  ( 4 min )

Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.  ( 10 min )

submitted by /u/otherworlderotic [link] [comments]  ( 8 min )

Is it possible to have Ai come with a generated lyrics and sings within the bpm + root note? Does this exist? i’ll like to know where and how. Ai is interesting. submitted by /u/Office_Flashy [link] [comments]  ( 8 min )

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Hilarious, comedic effort of an AI presidential debate going on now. https://www.twitch.tv/trumporbiden2024 submitted by /u/Smash_Factor [link] [comments]  ( 8 min )

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